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Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Published online by Cambridge University Press:  04 March 2014

Michael Hoy ,
Alexey S. Matveev  and
Andrey V. Savkin
Michael Hoy*
Affiliation:
School of Electrical Engineering and Telecommunication, University of New South Wales, Sydney, Australia
Alexey S. Matveev
Affiliation:
Department of Mathematics and Mechanics, Saint Petersburg University, St. Petersburg, Russia
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunication, University of New South Wales, Sydney, Australia
*
*Corresponding author. E-mail: [email protected]
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Summary

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We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

Keywords

Path planningNavigationMulti-robot systemsMotion planningMobile robotsControl of robotic systems
Type
Articles
Information
Robotica , Volume 33 , Issue 3 , March 2015 , pp. 463 - 497
Copyright
Copyright © Cambridge University Press 2014 

References

1.Abe, Y. and Yoshiki, M., “Collision Avoidance Method for Multiple Autonomous Mobile Agents by Implicit Cooperation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 3, Maui, HI, USA (2001) pp. 1207–1212.Google Scholar
2.Abichandani, P., Ford, G., Benson, H. Y. and Kam, M., “Mathematical Programming for Multi-Vehicle Motion Planning Problems,” Proceedings of the IEEE International Conference on Robotics and Automation, St Paul, MN, USA (2012) pp. 3315–3322.Google Scholar
3.Adinandra, S., Schreurs, E. and Nijmeijer, H., “A Practical Model Predictive Control for a Group of Unicycle Mobile Robots,” Proceedings of the 4th IFAC Conference on Nonlinear Model Predictive Control, Vol. 4, Leeuwenhorst, Netherlands (2012) pp. 472–477.Google Scholar
4.Ahmadi-Pajouh, M. A., Towhidkhah, F., Gharibzadeh, S. and Mashhadimalek, M., “Path planning in the hippocampo-prefrontal cortex pathway: An adaptive model based receding horizon planner,” Med. Hypotheses 68 (6), 1411–1415 (2007).CrossRefGoogle ScholarPubMed
5.Albagul, A. and Wahyudi, , “Dynamic Modeling and Adaptive Traction Control for Mobile Robots,” Proceedings of the 30th Annual Conference of IEEE Industrial Electronics Society, Vol. 1, Busan, Korea (2004) pp. 614–620.Google Scholar
6.Alonso-Mora, J., Breitenmoser, A., Beardsley, P. and Siegwart, R., “Reciprocal Collision Avoidance for Multiple Car-Like Robots,” Proceedings of the 2012 IEEE International Conference on Robotics and Automation, St Paul, MN, USA (2012) pp. 360–366.CrossRefGoogle Scholar
7.Althoff, D., Kuffner, J., Wollherr, D. and Buss, M., “Safety assessment of robot trajectories for navigation in uncertain and dynamic environments,” Auton. Robot. 32 (3), 285–302 (2012).CrossRefGoogle Scholar
8.Alvarez, J. C., Shkel, A. and Lumelsky, V., “Accounting for Mobile Robot Dynamics in Sensor-Based Motion Planning: Experimental Results,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 3, Lueven, Belgium (1998) pp. 2205–2210.Google Scholar
9.Andersson, S. B., “Curve tracking for rapid imaging in AFM,” IEEE Trans. Nanobiosci. 6 (4), 354–361 (2007).CrossRefGoogle ScholarPubMed
10.Arkin, R. C., “Motor schema based mobile robot navigation,” Int. J. Robot. Res. 8 (4), 92–112 (1989).CrossRefGoogle Scholar
11.Arkin, R. C., “Behavior-based robot navigation for extended domains,” Adapt. Behav. 1 (2), 201–225 (1992).CrossRefGoogle Scholar
12.Armesto, L., Girbes, V., Vincze, M., Olufs, S. and Munoz-Benavent, P., “Mobile Robot Obstacle Avoidance Based on Quasi-Holonomic Smooth Paths,” In: Advances in Autonomous Robotics (Lecture Notes in Computer Science), Vol. 7429 (Herrmann, G.et al., eds.) (Springer, Berlin, Germany, 2012) pp. 244–255.CrossRefGoogle Scholar
13.Augugliaro, F., Schoellig, A. P. and D'Andrea, R., “Generation of Collision-Free Trajectories for a Quadrocopter Fleet: A Sequential Convex Programming Approach,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Algarve, Portugal (2012) pp. 1917–1922.Google Scholar
14.Balakrishna, R. and Ghosal, A., “Modeling of slip for wheeled mobile robots,” IEEE Trans. Robot. Autom. 11 (1), 126–132 (1995).CrossRefGoogle Scholar
15.Balkcom, D. J., Kavathekar, P. A. and Mason, M. T., “Time-optimal trajectories for an omni-directional vehicle,” Int. J. Robot. Res. 25 (10), 985–999 (2006).CrossRefGoogle Scholar
16.Barat, C. and Rendas, M. J., “Benthic Boundary Tracking Using a Profiler Sonar,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, Las Vegas, NV, USA (Oct. 2003) pp. 830–835.Google Scholar
17.Baronov, D. and Baillieul, J., “Reactive Exploration Through Following Isolines in a Potential Field,” Proceedings of the American Control Conference, New York, NY, USA (Dec. 2007) pp. 2141–2146.Google Scholar
18.Bekris, K. E., Grady, D. K., Moll, M. and Kavraki, L. E., “Safe distributed motion coordination for second-order systems with different planning cycles,” Int. J. Robot. Res. 31 (2), 129–150 (2012).CrossRefGoogle Scholar
19.Bekris, K. E., Tsianos, K. I. and Kavraki, L. E., “Safe and distributed kinodynamic replanning for vehicular networks,” Mobile Netw. Appl. 14 (3), 292–308 (2009).CrossRefGoogle Scholar
20.Belkhouche, F., “Reactive path planning in a dynamic environment,” IEEE Trans. Robot. 25 (4), 902–911 (2009).CrossRefGoogle Scholar
21.Belkhous, S., Azzouz, A., Saad, M., Nerguizian, V. and Nerguizian, C., “A novel approach for mobile robot navigation with dynamic obstacles avoidance,” J. Intell. Robot. Syst. 44 (3), 187–201 (2005).CrossRefGoogle Scholar
22.Belta, C., Bicchi, A., Egerstedt, M., Frazzoli, E., Klavins, E. and Pappas, G. J., “Symbolic planning and control of robot motion [grand challenges of robotics],” IEEE Robot. Autom. Mag. 14 (1), 61–70 (2007).CrossRefGoogle Scholar
23.Bemporad, A. and Barcelli, D., “Decentralized model predictive control,” In: Lecture Notes in Control and Information Sciences, Vol. 406 (Bemporad, A., Heemels, M. and Johansson, M., eds.) (Springer, London, 2010) pp. 149–178.Google Scholar
24.Bemporad, A., Marco, M. D. and Tesi, A., “Sonar-based wall-following control of mobile robots,” ASME J. Dyn. Syst. Meas. Control 122 (1), 226–230 (2000).CrossRefGoogle Scholar
25.Bereg, S. and Kirkpatrick, D., “Curvature–bounded Traversals of Narrow Corridors,” Proceedings of the 21st Annual Symposium on Computational Geometry, Pisa, Italy (2005) pp. 278–287.Google Scholar
26.Kemp, M., Bertozzi, A. L. and Marthaler, D., “Multi–UUV Perimeter Surveillance,” Proceedings of the IEEE/OES Autonomous Underwater Vehicles Conference, Sebasco, ME, USA (Jun. 2004) pp. 102–107.Google Scholar
27.Besada-Portas, E., de la Torre, L., de la Cruz, J. M. and de Andres-Toro, B., “Evolutionary trajectory planner for multiple UAVs in realistic scenarios,” IEEE Trans. Robot. 26 (4), 619–634 (2010).CrossRefGoogle Scholar
28.Bevan, G., Gollee, H. and O'reilly, J., “Automatic lateral emergency collision avoidance for a passenger car,” Int. J. Control 80 (11), 1751–1762 (2007).CrossRefGoogle Scholar
29.Bicchi, A., Casalino, G. and Santilli, C., “Planning shortest bounded-curvature paths for a class of nonholonomic vehicles among obstacles,” J. Intell. Robot. Syst. 16 (4), 387–405 (1996).CrossRefGoogle Scholar
30.Blackmore, L., Ono, M. and Williams, B. C., “Chance-constrained optimal path planning with obstacles,” IEEE Trans. Robot. 27 (6), 1080–1094 (2011).CrossRefGoogle Scholar
31.Blanco, J.-L., Gonzalez, J. and Fernandez-Madrigal, J.-A., “Extending obstacle avoidance methods through multiple parameter-space transformations,” Auton. Robots 24 (1), 29–48 (2008).CrossRefGoogle Scholar
32.Bode, N. W., Wood, A. J. and Franks, D. W., “Social networks and models for collective motion in animals,” Behav. Ecol. Sociobiol. 65 (2), 117–130 (2011).CrossRefGoogle Scholar
33.Bonin-Font, F., Ortiz, A. and Oliver, G., “Visual navigation for mobile robots: A survey,” J. Intell. Robot. Syst. 53 (3), 263–296 (2008).CrossRefGoogle Scholar
34.Boquete, V., Garcia, R., Barea, R. and Mazo, M., “Neural control of the movements of a wheelchair,” J. Intell. Robot. Syst. 25 (3), 213–226 (1999).CrossRefGoogle Scholar
35.Bouraine, S., Fraichard, T. and Salhi, H., “Provably safe navigation for mobile robots with limited field-of-views in dynamic environments,” Auton. Robots 32 (3), 267–283 (2012).CrossRefGoogle Scholar
36.Brooks, A., Kaupp, T. and Makarenko, A., “Randomised MPC-based Motion-Planning for Mobile Robot Obstacle Avoidance,” Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 397–402.Google Scholar
37.Burian, E., Yoeger, D., Bradley, A. and Singh, H., “Gradient Search with Autonomous Underwater Vehicle using Scalar Measurements,” Proceedings of the IEEE Symposium on Underwater Vehicle Technology, Monterey, CA (Jun. 1996) pp. 86–98.CrossRefGoogle Scholar
38.Caccia, M., Bono, R. and Bruzzone, G., “Variable-configuration UUVs for marine science applications,” IEEE Robot. Autom. Mag. 6 (2), 22–32 (1999).CrossRefGoogle Scholar
39.Camhi, J. M. and Johnson, E. N., “High–frequency steering maneuvers mediated by tactile cues: Antennal wall-following in the cockroach,” J. Exp. Biol. 202 (5), 631–643 (1999).CrossRefGoogle ScholarPubMed
40.Canny, J., The Complexity of Robot Motion Planning (MIT Press, Cambridge, MA, 1988).Google Scholar
41.Carelli, R. and Freire, E. O., “Corridor navigation and wall-following stable control for sonar-based mobile robots,” Robot. Auton. Syst. 45 (12), 235–247 (2003).CrossRefGoogle Scholar
42.Casbeer, D. W., Kingston, D. B., Beard, R. W., McLain, T. W., Li, S. M. and Mehra, R., “Cooperative forest fire surveillance using a team of small unmanned air vehicles,” Int. J. Syst. Sci. 36 (6), 351–360 (2006).CrossRefGoogle Scholar
43.Casbeer, D. W., Li, S. M., Beard, R. W., McLain, T. W. and Mehra, R. K., “Forest Fire Monitoring Using Multiple Small UAVs,” Proceedings of the 2005 American Control Conference, Vol. 5, Minneapolis, MA, USA (2005) pp. 3530–3535.Google Scholar
44.Chakravarthy, A. and Ghose, D., “Obstacle avoidance in a dynamic environment: A collision cone approach,” IEEE Trans. Syst. Man Cybern. 28 (5), 562–574 (1998).CrossRefGoogle Scholar
45.Chang, D. E., Shadden, S. C., Marsden, J. E. and Olfati-Saber, R., “Collision Avoidance for Multiple Agent Systems,” Proceedings of the 42nd IEEE Conference on Decision and Control, Vol. 1, Maui, HI, USA (2003) pp. 539–543.Google Scholar
46.Cheng, T. M. and Savkin, A. V., “Decentralized control for mobile robotic sensor network self-deployment: Barrier and sweep coverage problems,” Robotica 29 (2), 283–294 (2011).CrossRefGoogle Scholar
47.Cheng, T. M. and Savkin, A. V., “Self-deployment of mobile robotic sensor networks for multilevel barrier coverage,” Robotica 30 (4), 661–669 (2012).CrossRefGoogle Scholar
48.Cheng, T. M., Savkin, A. V. and Javed, F., “Decentralized control of a group of mobile robots for deployment in sweep coverage,” Robot. Auton. Syst. 59 (7–8), 497–507 (2011).CrossRefGoogle Scholar
49.Chitsaz, H., LaValle, S. M., Balkcom, D. J. and Mason, M. T., “Minimum wheel-rotation paths for differential-drive mobile robots,” Int. J. Robot. Res. 28 (1), 66–80 (2009).CrossRefGoogle Scholar
50.Chung, W., Kim, S., Choi, M., Choi, J., Kim, H., Moon, C-B. and Song, J-B., “Safe navigation of a mobile robot considering visibility of environment,” IEEE Trans. Ind. Electron. 56 (10), 3941–3950 (2009).CrossRefGoogle Scholar
51.Chunyu, J., Qu, Zh., Pollak, E. and Falash, M., “Reactive Target-tracking Control with Obstacle Avoidance of Unicycle-type Mobile Robots in a Dynamic Environment,” American Control Conference, Baltimore, MD (Jun. 2010) pp. 1190–1195.Google Scholar
52.Cifuentes, S., Giron-Sierra, J. M. and Jimenez, J., “Robot navigation based on discrimination of artificial fields: Application to single robots,” Adv. Robot. 26 (5–6), 605–626 (2012).CrossRefGoogle Scholar
53.Cochran, J. and Krstic, M., “Nonholonomic source seeking with tuning of angular velocity,” IEEE Trans. Autom. Control 54 (4), 717–731 (2009).CrossRefGoogle Scholar
54.Consolini, L. and Tosques, M., “A path following problem for a class of non-holonomic control systems with noise,” Automatica 41 (6), 1009–1016 (2005).CrossRefGoogle Scholar
55.Cowlagi, R. V. and Tsiotras, P., “Hierarchical motion planning with dynamical feasibility guarantees for mobile robotic vehicles,” IEEE Trans. Robot. 28 (2), 379–395 (2012).CrossRefGoogle Scholar
56.Cui, R., Gao, B. and Guo, J., “Pareto-optimal coordination of multiple robots with safety guarantees,” Auton. Robots 32 (3), 189–205 (2012).CrossRefGoogle Scholar
57.Dadkhah, N. and Mettler, B., “Survey of motion planning literature in the presence of uncertainty: Considerations for UAV guidance,” J. Intell. Robot. Syst. 65 (1), 233–246 (2012).CrossRefGoogle Scholar
58.De Schutter, J., De Laet, T., Rutgeerts, J., Decra, W., Smits, R., Aertbelian, E., Claes, K. and Bruyninckx, H., “Constraint-based task specification and estimation for sensor-based robot systems in the presence of geometric uncertainty,” Int. J. Robot. Res. 26 (5), 433–455 (2007).CrossRefGoogle Scholar
59.Defoort, M., Kokosy, A., Floquet, T., Perruquetti, W. and Palos, J., “Motion planning for cooperative unicycle-type mobile robots with limited sensing ranges: A distributed receding horizon approach,” Robot. Auton. Syst. 57 (11), 1094–1106 (2009).CrossRefGoogle Scholar
60.Defoort, M., Palos, J., Kokosy, A., Floquet, T. and Perruquetti, W., “Performance-based reactive navigation for non-holonomic mobile robots,” Robotica 27 (2), 281–290 (2009).CrossRefGoogle Scholar
61.Deng, M., Inoue, A., Shibata, Y., Sekiguchi, K. and Ueki, N., “An Obstacle Avoidance Method for Two Wheeled Mobile Robot,” Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control (2007), pp. 689–692.Google Scholar
62.Desaraju, V. and How, J., “Decentralized path planning for multi-agent teams with complex constraints,” Auton. Robots 32 (4), 385–403 (2012).CrossRefGoogle Scholar
63.DeSouza, G. N. and Kak, A. C., “Vision for mobile robot navigation: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. 2 (24), 237–267 (2002).CrossRefGoogle Scholar
64.Diankov, R. and Kuffner, J., “Randomized Statistical Path Planning,” Proceedings of the 2007 IEEE/RSJ International Conference on Robots and Systems, San Diego, CA, USA (2007) pp. 1–6.Google Scholar
65.Dimarogonas, D. V. and Kyriakopoulos, K. J., “Decentralized navigation functions for multiple robotic agents with limited sensing capabilities,” J. Intell. Robot. Syst. 48 (3), 411–433 (2007).CrossRefGoogle Scholar
66.Dimarogonas, D. V. and Kyriakopoulos, K. J., “Connectedness preserving distributed swarm aggregation for multiple kinematic robots,” IEEE Trans. Robot. 24 (5), 1213–1223 (2008).CrossRefGoogle Scholar
67.Dimarogonas, D. V., Loizou, S. G., Kyriakopoulos, K. J. and Zavlanos, M. M., “A feedback stabilization and collision avoidance scheme for multiple independent non-point agents,” Automatica 42 (2), 229–243 (2006).CrossRefGoogle Scholar
68.Douillard, B., Fox, D., Ramos, F. and Durrant-Whyte, H., “Classification and semantic mapping of urban environments,” Int. J. Robot. Res. 30 (1), 5–32 (2011).CrossRefGoogle Scholar
69.Du Toit, N. E. and Burdick, J. W., “Robot motion planning in dynamic, uncertain environments,” IEEE Trans. Robot. 28 (1), 101–115 (2012).CrossRefGoogle Scholar
70.Dubins, L. E., “On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal positions and tangents,” Am. J. Math. 79 (3), 497–516 (1957).CrossRefGoogle Scholar
71.Durrant-Whyte, H. and Bailey, T., “Simultaneous localization and mapping: Part I,” IEEE Robot. Autom. Mag. 13 (2), 99–110 (2006).CrossRefGoogle Scholar
72.Ekanayake, S. W. and Pathirana, P. N., “Formations of robotic swarm: An artificial force based approach,” Int. J. Adv. Robot. Syst. 6 (1), 7–24 (2009).CrossRefGoogle Scholar
73.Fahimi, F., Nataraj, C. and Ashrafiuon, H., “Real-time obstacle avoidance for multiple mobile robots,” Robotica 27 (2), 189–198 (2009).CrossRefGoogle Scholar
74.Farrokhsiar, M. and Najjaran, H., “An Unscented Model Predictive Control Approach to the Formation Control of Nonholonomic Mobile Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, St Paul, MN, USA (2012) pp. 1576–1582.Google Scholar
75.Fazli, S. and Kleeman, L., “Wall following and Obstacle Avoidance Results from A Multi-DSP Sonar Ring on a Mobile Robot,” IEEE International Conference Mechatronics and Automation, Vol. 1, Niagara Falls, Canada (Jul. 2005) pp. 432–437.Google Scholar
76.Fernandez, J. L., Sanz, R., Benayas, J. A. and Diaguez, A. R., “Improving collision avoidance for mobile robots in partially known environments: The beam curvature method,” Robot. Auton. Syst. 46 (4), 205–219 (2004).CrossRefGoogle Scholar
77.Ferrara, A. and Rubagotti, M., “Sliding Mode Control of a Mobile Robot for Dynamic Obstacle Avoidance Based on a Time-varying Harmonic Potential Field,” ICRA 2007 Workshop: Planning, Perception and Navigation for Intelligent Vehicles, Rome, Italy (Apr. 2007).Google Scholar
78.Fiorini, P. and Shiller, Z., “Time Optimal Trajectory Planning in Dynamic Environments,” Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA (1996) pp. 1553–1558.CrossRefGoogle Scholar
79.Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,” Int. J. Robot. Res. 17 (7), 760–772 (1998).CrossRefGoogle Scholar
80.Flierl, G., Grunbaum, D., Levin, S. and Olson, D., “From individuals to aggregations: The interplay between behavior and physics,” J. Theor. Biol. 196 (4), 397–454 (1999).CrossRefGoogle ScholarPubMed
81.Foka, A. and Trahanias, P., “Probabilistic autonomous robot navigation in dynamic environments with human motion prediction,” Int. J. Soc. Robot. 2 (1), 79–94 (2010).CrossRefGoogle Scholar
82.Fossen, T., Guidance and Control of Ocean Vehicles (Wiley, NY, 1994).Google Scholar
83.Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag. 4 (1), 23–33 (1997).CrossRefGoogle Scholar
84.Fraichard, T., “Trajectory planning in a dynamic workspace: A state–time space approach,” Adv. Robot. 13 (1), 75–94 (1999).CrossRefGoogle Scholar
85.Fraichard, T. and Asama, H., “Inevitable Collision States. A Step Towards Safer Robots?” IEEE International Conference on Intelligent Robots and Systems, Vol. 1, Las Vegas, NV, USA (2003) pp. 388–393.Google Scholar
86.Fujimori, A., Teramoto, M., Nikiforuk, P. N. and Gupta, M. M., “Cooperative collision avoidance between multiple mobile robots,” J. Robot. Syst. 17 (7), 347–363 (2000).3.0.CO;2-A>CrossRefGoogle Scholar
87.Gabriely, Y. and Rimon, E., “CBUG: A quadratically competitive mobile robot navigation algorithm,” IEEE Trans. Robot. 24 (6), 1451–1457 (2008).CrossRefGoogle Scholar
88.Galicki, M., “Collision-free control of an omni-directional vehicle,” Robot. Auton. Syst. 57 (9), 889–900 (2009).CrossRefGoogle Scholar
89.Garrido, S., Moreno, L., Blanco, D. and Jurewicz, P., “Path planning for mobile robot navigation using voronoi diagram and fast marching,” Int. J. Robot. Autom. 2 (1), 42–64 (2011).Google Scholar
90.Ge, S. S. and Cui, Y. J., “New potential functions for mobile robot path planning,” IEEE Trans. Robot. Autom. 16 (5), 615–620 (2000).CrossRefGoogle Scholar
91.Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,” Auton. Robots 13 (3), 207–222 (2002).CrossRefGoogle Scholar
92.Ge, S. S., Lai, X. and Mamun, A. A., “Boundary following and globally convergent path planning using instant goals,” IEEE Trans. Syst. Man Cybern. 35 (2), 240–254 (2005).CrossRefGoogle ScholarPubMed
93.Ge, S. S., Lai, X. and Mamun, A. A., “Sensor-based path planning for nonholonomic mobile robots subject to dynamic constraints,” Robot. Auton. Syst. 55 (7), 513–526 (2007).CrossRefGoogle Scholar
94.Gecks, T. and Henrich, D., “Sensor-based Online Planning of Time-optimized Paths in Dynamic Environments,” In: Advances in Robotics Research (Krager, T. and Wahl, F. M., eds.) (Springer, Berlin–Heidelberg, 2009) pp. 53–63.CrossRefGoogle Scholar
95.Ghrist, R. W. and Koditschek, D. E., “Safe cooperative robot dynamics on graphs,” SIAM J. Control Optim. 40 (5), 1556–1575 (2002).CrossRefGoogle Scholar
96.Girard, A., Howell, A. S. and Hedrick, J. K., “Border Patrol and Surveillance Missions using Multiple Unmanned Air Vehicles,” Proceedings of the 43th IEEE Conference on Decision and Control, Paradise Island, Bahamas (2004) pp. 620–625.Google Scholar
97.Goerzen, C., Kong, Z. and Mettler, B., “A survey of motion planning algorithms from the perspective of autonomous UAV guidance,” J. Intell. Robot. Syst. 57 (1–4), 65–100 (2009).CrossRefGoogle Scholar
98.Gomez, M., Gonzalez, R. V., Martinez-Marin, T., Meziat, D. and Sanchez, S., “Optimal motion planning by reinforcement learning in autonomous mobile vehicles,” Robotica 30 (2), 159–170 (2012).CrossRefGoogle Scholar
99.Gonzalez, R., Fiacchini, M., Guzman, J. L., Alamo, T. and Rodriguez, F., “Robust tube-based predictive control for mobile robots in off-road conditions,” Robot. Auton. Syst. 59 (10), 711–726 (2011).CrossRefGoogle Scholar
100.Gracia, L. and Tornero, J., “Kinematic modeling and singularity of wheeled mobile robots,” Adv. Robot. 21 (7), 793–816 (2007).CrossRefGoogle Scholar
101.Gracia, L. and Tornero, J., “Kinematic modeling of wheeled mobile robots with slip,” Adv. Robot. 21 (11), 1253–1279 (2007).CrossRefGoogle Scholar
102.Grancharova, A., Gratli, E. I. and Johansen, T. A., “Distributed MPC-based Path Planning for UAVs Under Radio Communication Path Loss Constraints,” Proceedings of the IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control, Wurzburg, Germany (2012) pp. 254–259.Google Scholar
103.Gratli, E. and Johansen, T., “Path planning for UAVs under communication constraints using SPLAT! and MILP,” J. Intell. Robot. Syst. 65 (1), 265–282 (2012).CrossRefGoogle Scholar
104.Green, W. E. and Oh, P. Y., “Optic-flow-based collision avoidance,” IEEE Robot. Autom. Mag. 15 (1), 96–103 (2008).CrossRefGoogle Scholar
105.Hernandez-Martinez, E. G. and Aranda-Bricaire, E., “Convergence and Collision Avoidance in Formation Control: A Survey of the Artificial Potential Functions Approach,” In: Multi-Agent Systems-Modeling, Control, Programming, Simulations and Applications. InTech (Alkhateeb, F., Al Maghayreh, E. and Abu Doush, I., eds) (2011).Google Scholar
106.Hoffmann, G. M. and Tomlin, C. J., “Decentralized Cooperative Collision Avoidance for Acceleration Constrained Vehicles,” Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico (2008) pp. 4357–4363.Google Scholar
107.Horn, S. and Janschek, K., “A Set-based Global Dynamic Window Algorithm for Robust and Safe Mobile Robot Path Planning,” Proceedings of the 41st International Symposium on Robotics and the 6th German Conference on Robotics Munich, Germany (2010) pp. 1–7.Google Scholar
108.Hoy, M., “Deadlock Resolution for Navigation of Wheeled Robots in Continuous State-space,” Proceedings of the International Conference on Automation, Robotics, Control and Vision, Guangzhou, China (2012) pp. 130–135.Google Scholar
109.Hoy, M., Matveev, A. S., Garratt, M. and Savkin, A. V., “Collision free navigation of an autonomous unmanned helicopter in unknown urban environments: Sliding mode and MPC approaches,” Robotica 30 (4), 537–550 (2012).CrossRefGoogle Scholar
110.Hoy, M., Matveev, A. S. and Savkin, A. V., “Collision free cooperative navigation of multiple wheeled robots in unknown cluttered environments,” Robot. Auton. Syst. 60 (10), 1253–1266 (2012).CrossRefGoogle Scholar
111.Hoy, M. and Savkin, A. V., “A method of boundary following by a wheeled mobile robot based on sampled range information,” J. Intell. Robot. Syst. 72 (3–4), 463–482 (2013).CrossRefGoogle Scholar
112.Huang, L., “Wall-following control of an infrared sensors guided wheeled mobile robot,” Int. J. Intell. Syst. Technol. Appl. 7 (1), 106–117 (2009).Google Scholar
113.Huang, W. H., Fajen, B. R., Fink, J. R. and Warren, W. H., “Visual navigation and obstacle avoidance using a steering potential function,” Robot. Auton. Syst. 54 (4), 288–299 (2006).CrossRefGoogle Scholar
114.Innocenti, M., Pollini, L. and Turra, D., “A fuzzy approach to the guidance of unmanned air vehicles tracking moving targets,” IEEE Trans. Control Syst. Technol., 16 (6), 1125–1137 (2008).CrossRefGoogle Scholar
115.Jefferies, M. E. and Yeap, W., eds., Robotics and Cognitive Approaches to Spatial Mapping, Vol. 38 (Springer, Berlin Heidelberg, 2008).CrossRefGoogle Scholar
116.Joshi, A., Ashley, T., Huang, Y. R. and Bertozzi, A. L., “Experimental Validation of Cooperative Environmental Boundary Tracking with On-board Sensors,” Proceedings of the American Control Conference, St Louis, MO, USA (Jun. 2009) pp. 2630–2635.Google Scholar
117.Kallem, V., Komoroski, A. T. and Kumar, V., “Sequential composition for navigating a nonholonomic cart in the presence of obstacles,” IEEE Trans. Robot. 27 (6), 1152–1159 (2011).CrossRefGoogle Scholar
118.Kamon, I., Rimon, E. and Rivlin, E., “A range-sensor based navigation algorithm,” Int. J. Robot. Res. 17 (9), 934–953 (1991).CrossRefGoogle Scholar
119.Kamon, I., Rimon, E. and Rivlin, E., “Tangentbug: A range-sensor-based navigation algorithm,” Int. J. Robot. Res. 17 (9), 934–953 (1998).CrossRefGoogle Scholar
120.Kamon, I. and Rivlin, E., “Sensory-based motion planning with global proofs,” IEEE Trans. Robot. Autom. 13 (6), 814–822 (1997).CrossRefGoogle Scholar
121.Karaman, S. and Frazzoli, E., “Sampling–based algorithms for optimal motion planning,” Int. J. Robot. Res. 30 (7), 846–894 (2011).CrossRefGoogle Scholar
122.Bertozzi, A. L., Kemp, M. and Marthaler, D., “Determining Environmental Boundaries: Asynchronous Communication and Physical Scales,” In: Cooperative Control (Kumar, V., Leonard, N. E. and Morse, A. S., eds.) (Springer Verlag, Berlin, 2004) pp. 25–42.CrossRefGoogle Scholar
123.Kendoul, F., “Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems,” J. Field Robot. 29 (2), 315–378 (2012).CrossRefGoogle Scholar
124.Kim, D. H. and Shin, S., “New repulsive potential functions with angle distributions for local path planning,” Adv. Robot. 20 (1), 25–48 (2006).CrossRefGoogle Scholar
125.Kim, J., Zhang, F. and Egerstedt, M., “Curve tracking control for autonomous vehicles with rigidly mounted range sensors,” J. Intell. Robot. Syst. 56 (2), 177–197 (2009).CrossRefGoogle Scholar
126.Kim, S., Russel, J. and Koo, K., “Construction robot path-planning for earthwork operations,” J. Comput. Civ. Eng. 17 (2), 97–104 (2003).CrossRefGoogle Scholar
127.Ko, N. Y. and Simmons, R. G., “The Lane-curvature Method for Local Obstacle Avoidance,” Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 3, Victoria, Canada (1998) pp. 1615–1621.Google Scholar
128.Koenig, S. and Likhachev, M., “Fast replanning for navigation in unknown terrain,” IEEE Trans. Robot. 21 (3), 354–363 (2005).CrossRefGoogle Scholar
129.Kozlowski, K., Robot Motion and Control (Springer, London, 2009).Google Scholar
130.Krishnamurthy, P. and Khorrami, F., “GODZILA: A low–resource algorithm for path planning in unknown environments,” J. Intell. Robot. Syst. 48 (3), 357–373 (2007).CrossRefGoogle Scholar
131.Krontiris, A. and Bekris, K. E., “Using Minimal Communication to Improve Decentralized Conflict Resolution for Non-holonomic Vehicles,” Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA (2011) pp. 3235–3240.Google Scholar
132.Kuchar, J. K. and Yang, L. C., “A review of conflict detection and resolution modeling methods,” IEEE Trans. Intell. Trans. Syst. 1 (4), 179–189 (2000).CrossRefGoogle Scholar
133.Kulić, R. and Vukić, Z., “Methodology of concept control synthesis to avoid unmoving and moving obstacles,” J. Intell. Robot. Syst. 45 (1), 267–294 (2006).CrossRefGoogle Scholar
134.Kurniawati, H., Du, Y., Hsu, D. and Lee, W. S., “Motion planning under uncertainty for robotic tasks with long time horizons,” Int. J. Robot. Res. 30 (3), 308–323 (2011).CrossRefGoogle Scholar
135.Kuwata, Y. and How, J. P., “Cooperative distributed robust trajectory optimization using receding horizon MILP,” IEEE Trans. Control Syst. Technol. 19 (2), 423–431 (2011).CrossRefGoogle Scholar
136.Kuwata, Y., Richards, A., Schouwenaars, T. and How, J. P., “Distributed robust receding horizon control for multivehicle guidance,” IEEE Trans. Control Syst. Technol. 15 (4), 627–641 (2007).CrossRefGoogle Scholar
137.Lalish, E. and Morgansen, K., “Distributed reactive collision avoidance,” Auton. Robots 32 (3), 207–226 (2012).CrossRefGoogle Scholar
138.Lalish, E., Morgansen, K. A. and Tsukamaki, T., “Decentralized Reactive Collision Avoidance for Multiple Unicycle-type Vehicles,” Proceedings of the American Control Conference, Seattle, WA, USA (2008) pp. 5055–5061.Google Scholar
139.Langer, R., Coelho, L. and Oliveira, G., “K-bug, A New Bug Approach for Mobile Robot's Path Planning,” Proceedings of the IEEE International Conference on Control Applications, Singapore (Oct. 2007) pp. 403–408.Google Scholar
140.Langson, W., Chryssochoos, I., Rakovic, S. V. and Mayne, D. Q., “Robust model predictive control using tubes,” Automatica 40 (1), 125–133 (2004).CrossRefGoogle Scholar
141.Lapierre, L. and Jouvencel, B., “Robust nonlinear path-following control of an AUV,” IEEE J. Ocean. Eng. 33 (2), 89–102 (2008).CrossRefGoogle Scholar
142.Lapierre, L. and Zapata, R., “A guaranteed obstacle avoidance guidance system,” Auton. Robots 32 (3), 177–187 (2012).CrossRefGoogle Scholar
143.Lapierre, L., Zapata, R. and Lepinay, P., “Combined path-following and obstacle avoidance control of a wheeled robot,” Int. J. Robot. Res. 26 (4), 361–375 (2007).CrossRefGoogle Scholar
144.Large, F., Lauger, C. and Shiller, Z., “Navigation among moving obstacles using the NLVO: Principles and applications to intelligent vehicles,” Auton. Robots 19 (2), 159–171 (2005).CrossRefGoogle Scholar
145.Latombe, J. C., Robot Motion Planning (Kluwer Academic Publishers, London, 1991).CrossRefGoogle Scholar
146.Lau, B., Sprunk, C. and Burgard, W., “Kinodynamic Motion Planning for Mobile Robots Using Splines,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St Louis, MO, USA (2009) pp. 2427–2433.Google Scholar
147.Laubach, S. L. and Burdick, J. W., “An Autonomous Sensor-Based Path-Planner for Planetary Microrovers,” Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI, USA (May 1999) pp. 347–354.Google Scholar
148.Lee, D. N., “Guiding movements by coupling taus,” Ecol. Psychol. 10 (3–4), 221–250 (1998).CrossRefGoogle Scholar
149.Lee, H., Utkin, V. I. and Malinin, A., “Chattering reduction using multiphase sliding mode control,” Int. J. Control 82 (9), 1720–1737 (2009).CrossRefGoogle Scholar
150.Lee, K. B. and Han, M. H., “Lane-following method for high speed autonomous vehicles,” Int. J. Automot. Technol. 9 (5), 607–613 (2008).CrossRefGoogle Scholar
151.Li, W. and Cassandras, C. G., “A cooperative receding horizon controller for multivehicle uncertain environments,” IEEE Trans. Autom. Control 51 (2), 242–257 (2006).CrossRefGoogle Scholar
152.Lindemann, S. R., Hussein, I. I. and LaValle, S. M., “Real Time Feedback Control for Nonholonomic Mobile Robots with Obstacles,” Proceedings of the 45th IEEE Conference on Decision and Control, San Diego, CA, USA (Dec. 2006) pp. 2406–2411.CrossRefGoogle Scholar
153.Liu, Y-H. and Arimoto, S., “Path planning using a tangent graph for mobile robots among polygonal and curved obstacles,” Int. J. Robot. Res. 11 (4), 376–382 (1992).CrossRefGoogle Scholar
154.Hsieh, M. A., Loizou, S. and Kumar, V., “Stabilization of Multiple Robots on Stable Orbits via Local Sensing,” Proceedings of the IEEE Conference on Robotics and Automation, Rome, Italy (Apr. 2007) pp. 2312–2317.Google Scholar
155.Loizou, S. G. and Kyriakopoulos, K. J., “Navigation of multiple kinematically constrained robots,” IEEE Trans. Robot. 24 (1), 221–231 (2008).CrossRefGoogle Scholar
156.Lopez, A. S., Zapata, R. and Osorio-Lama, M. A., “Sampling-based motion planning: A survey,” Comput. Sistem. 12 (1), 5–24 (2008).Google Scholar
157.Low, E. M., Manchester, I. R. and Savkin, A. V., “A biologically inspired method for vision-based docking of wheeled mobile robots,” Robot. Auton. Syst. 55 (10), 769–784 (2007).CrossRefGoogle Scholar
158.Lumelsky, V. and Stepanov, A. A., “Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape,” Algorithmica 2 (1), 403–430 (1987).CrossRefGoogle Scholar
159.Lumelsky, V. and Tiwari, S., “An Algorithm for Maze Searching with Azimuth Input,” Proceedings of the IEEE Conference on Robotics and Automation, San Diego, CA, USA (May 1991) pp. 111–116.Google Scholar
160.Lumelsky, V. J. and Skewis, T., “Incorporating range sensing in the robot navigation function,” IEEE Trans. Syst. Man Cybern. 20 (5), 1058–1069 (1990).CrossRefGoogle Scholar
161.Lumelsky, V. J. and Stepanov, A. A., “Dynamic path planning for a mobile automaton with limited information on the environment,” IEEE Trans. Autom. Control 31 (11), 1058–1063 (1986).CrossRefGoogle Scholar
162.Maes, P. and Brooks, R. A., “Learning to Coordinate Behaviors,” Proceedings of the AAAI, Boston, MA (1990) pp. 796–802.Google Scholar
163.Magid, E. and Rivlin, E., “Cautiousbug: A Competitive Algorithm for Sensor-based Robot Navigation,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (Sep. 2004) pp. 2757–2762.Google Scholar
164.Magni, L., Raimondo, D. and Allgower, F., Nonlinear Model Predictive Control: Towards New Challenging Applications (Springer-Verlag, Berlin, Germany, 2009).CrossRefGoogle Scholar
165.Malisoff, M., Mazenc, F. and Zhang, F., “Input-to-state Stability for Curve Tracking Control: A Constructive Approach,” Proceedings of the American Control Conference, San Francisco, CA (USA, 2011) pp. 1984–1989.Google Scholar
166.Manchester, I. R. and Savkin, A. V., “Circular navigation missile guidance with incomplete information and uncertain autopilot model,” J. Guid. Control Dyn. 27 (6), 1076–1083 (2004).CrossRefGoogle Scholar
167.Manchester, I. R. and Savkin, A. V., “Circular navigation guidance law for precision missile/target engagement,” J. Guid. Control Dyn. 29 (2), 1287–1292 (2006).CrossRefGoogle Scholar
168.Manor, G. and Rimon, E., “High-speed Navigation of a Uniformly Braking Mobile Robot Using Position–Velocity Configuration Space,” Proceedings of the IEEE International Conference on Robotics and Automation, St Paul, MN, USA (2012) pp. 193–199.Google Scholar
169.Marthaler, D. and Bertozzi, A. L., “Tracking Environmental Level Sets with Autonomous Vehicles,” In: Recent Developments in Cooperative Control and Optimization (Butenko, S., Murphey, R. and Pardalos, P. M., eds.), Vol. 3 (Kluwer, Boston, MA, 2003).Google Scholar
170.Masehian, E. and Katebi, Y., “Robot motion planning in dynamic environments with moving obstacles and target,” Int. J. Mech. Syst. Sci. Eng. 1 (1), 20–25 (2007).Google Scholar
171.Masoud, A., “Kinodynamic motion planning,” IEEE Robot. Autom. Mag. 17 (1), 85–99 (2010).CrossRefGoogle Scholar
172.Masoud, A., “A harmonic potential approach for simultaneous planning and control of a generic UAV platform,” J. Intell. Robot. Syst. 65 (1), 153–173 (2012).CrossRefGoogle Scholar
173.Mastellone, S., Stipanovic, D. M., Graunke, C. R., Intlekofer, K. A. and Spong, M. W., “Formation control and collision avoidance for multi-agent non-holonomic systems: Theory and experiments,” Int. J. Robot. Res. 27 (1), 107–126 (2008).CrossRefGoogle Scholar
174.Mastrogiovanni, F., Sgorbissa, A. and Zaccaria, R., “Robust navigation in an unknown environment with minimal sensing and representation,” IEEE Trans. Syst. Man Cybern. 39 (1), 212–229 (2009).CrossRefGoogle Scholar
175.Mataric, M. J., “Behavior-based Control: Main Properties and Implications,” Proceedings of the IEEE International Conference on Robotics and Automation Nice, France (1992) pp. 46–54.Google Scholar
176.Matveev, A. S., Hoy, M., Katupitiya, J. and Savkin, A. V., “Nonlinear sliding mode control of an unmanned agricultural tractor in the presence of sliding and control saturation,” Robot. Auton. Syst. 61 (9), 973–987 (2013).CrossRefGoogle Scholar
177.Matveev, A. S., Hoy, M. and Savkin, A. V., “The problem of boundary following by a unicycle-like robot with rigidly mounted sensors,” Robot. Auton. Syst. 61 (3), 312–327 (2013).CrossRefGoogle Scholar
178.Matveev, A. S., Hoy, M. C. and Savkin, A. V., “A method for reactive navigation of nonholonomic robots in maze-like environments,” Automatica 49 (5), 1268–1274 (2013).CrossRefGoogle Scholar
179.Matveev, A. S. and Savkin, A. V., “The problem of state estimation via asynchronous communication channels with irregular transmission times,” IEEE Trans. Autom. Control 48 (4), 670–676 (2003).CrossRefGoogle Scholar
180.Matveev, A. S. and Savkin, A. V., Estimation and Control over Communication Networks (Birkhauser, Boston, 2009).Google Scholar
181.Matveev, A. S., Teimoori, H. and Savkin, A. V., “A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance,” Automatica 47 (3), 515–514 (2011).CrossRefGoogle Scholar
182.Matveev, A. S., Teimoori, H. and Savkin, A. V., “Navigation of a unicycle-like mobile robot for environmental extremum seeking,” Automatica 47 (1), 85–91 (2011).CrossRefGoogle Scholar
183.Matveev, A. S., Teimoori, H. and Savkin, A. V., “Range-only measurements based target following for wheeled mobile robots,” Automatica 47 (1), 177–184 (2011).CrossRefGoogle Scholar
184.Matveev, A. S., Teimoori, H. and Savkin, A. V., “Method for tracking of environmental level sets by a unicycle-like vehicle,” Automatica 48 (9), 2252–2261 (2012).CrossRefGoogle Scholar
185.Matveev, A. S., Wang, C. and Savkin, A. V., “Real-time navigation of mobile robots in problems of border patrolling and avoiding collisions with moving and deforming obstacles,” Robot. Auton. Syst. 60 (6), 769–788 (2012).CrossRefGoogle Scholar
186.Mayne, D. Q., Kerrigan, E. C., van Wyk, E. J. and Falugi, P., “Tube-based robust nonlinear model predictive control,” Int. J. Robust Nonlinear Control 21 (11), 1341–1353 (2011).CrossRefGoogle Scholar
187.Mayne, D. Q. and Rakovic, S., “Model predictive control of constrained piecewise affine discrete-time systems,” Int. J. Robust Nonlinear Control 13 (3–4), 261–279 (2003).CrossRefGoogle Scholar
188.Micaelli, A. and Samson, C., “Trajectory tracking for unicycle-type and two-steering wheels mobile robots,” Technical Report INRIA: Technical Report No: 2097, Institut national de recherche en informatique et en automatique (1993).Google Scholar
189.Minguez, J. and Montano, L., “The Ego-Kinodynamic Space: Collision Avoidance for Any Shape Mobile Robots with Kinematic and Dynamic Constraints,” Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, Las Vegas, NV, USA (2003) pp. 637–643.Google Scholar
190.Minguez, J. and Montano, L., “Nearness diagram (ND) navigation: Collision avoidance in troublesome scenarios,” IEEE Trans. Robot. Autom. 20 (1), 45–59 (2004).CrossRefGoogle Scholar
191.Minguez, J. and Montano, L., “Sensor-based robot motion generation in unknown, dynamic and troublesome scenarios,” Robot. Auton. Syst. 52 (4), 290–311 (2005).CrossRefGoogle Scholar
192.Minguez, J. and Montano, L., “Extending collision avoidance methods to consider the vehicle shape, kinematics, and dynamics of a mobile robot,” IEEE Trans. Robot. 25 (2), 367–381 (2009).CrossRefGoogle Scholar
193.Moghadam, P., Wijesoma, W. S. and Dong, J. F., “Improving Path Planning and Mapping Based on Stereo Vision and Lidar,” Proceedings of the International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam (2008).Google Scholar
194.Montesano, L., Minguez, J. and Montano, L., “Modeling dynamic scenarios for local sensor-based motion planning,” Auton. Robots 25 (3), 231–251 (2008).CrossRefGoogle Scholar
195.Morgan, D., Chung, S.-J. and Hadaegh, F. Y., “Decentralized Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming,” Proceedings of the AAS/AIAA Space Flight Mechanics Conference, Kauai, HI, USA (2013).Google Scholar
196.Nak, Y. K. and Simmons, R., “The Lane-Curvature Method for Local Obstacle Avoidance,” IEEE International Conference on Robotics and Automation, Vol. 3, Lueven, Belgium (Nov. 1998) pp. 1615–1621.Google Scholar
197.Ng, J., An Analysis of Mobile Robot Navigation Algorithms in Unknown Environments, Ph.D. Thesis, The University of Western Australia, Perth, Australia 2010.Google Scholar
198.Ng, J. and Braunl, T., “Performance comparison of bug navigation algorithms,” J. Intell. Robot. Syst. 50 (1), 73–84 (2007).CrossRefGoogle Scholar
199.Nishi, T., Ando, M. and Konishi, M., “Distributed route planning for multiple mobile robots using an augmented lagrangian decomposition and coordination technique,” IEEE Trans. Robot. 21 (6), 1191–1200 (2005).CrossRefGoogle Scholar
200.Noborio, H., “A sufficient condition for designing a family of sensor based deadlock free planning algorithms,” Adv. Robot. 7 (5), 413–433 (1993).CrossRefGoogle Scholar
201.Noborio, H. and Yoshioka, T., “An On-line and Deadlock-free Path Planning Algorithm Based on World Topology,” Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, Yokohama, Japan (Dec. 1993) pp. 1425–1430.Google Scholar
202.Ogren, P. and Leonard, N., “A Tractable Convergent Dynamic Window Approach to Obstacle Avoidance,” Proceedings of IEEE International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (2002) pp. 595–600.Google Scholar
203.Ogren, P. and Leonard, N. E., “A convergent dynamic window approach to obstacle avoidance,” IEEE Trans. Robot. 21 (2), 188–195 (2005).CrossRefGoogle Scholar
204.Ohki, T., Nagatani, K. and Yoshida, K., “Local path planner for mobile robot in dynamic environment based on distance time transform method,” Adv. Robot. 26 (14), 1623–1647 (2012).CrossRefGoogle Scholar
205.Ordonez, C., Collins, E. G. Jr., Selekwa, M. F. and Dunlap, D. D., “The virtual wall approach to limit cycle avoidance for unmanned ground vehicles,” Robot. Auton. Syst. 56 (8), 645–657 (2008).CrossRefGoogle Scholar
206.Ostertag, E., “An improved path-following method for mixed h-2/h-infinity controller design,” IEEE Trans. Autom. Control 53 (8), 1967–1971 (2008).CrossRefGoogle Scholar
207.Owen, E. and Montano, L., “A Robocentric Motion Planner for Dynamic Environments Using the Velocity Space,” IEEE International Conference on Intelligent Robots and Systems, Vol. 1, Beijing, China (2006) pp. 2833–2838.Google Scholar
208.Pallottino, L., Scordio, V. G., Bicchi, A. and Frazzoli, E., “Decentralized cooperative policy for conflict resolution in multivehicle systems,” IEEE Trans. Robot. 23 (6), 1170–1183 (2007).CrossRefGoogle Scholar
209.Park, J. M., Kim, D. W., Yoon, Y. S., Kim, H. J. and Yi, K. S., “Obstacle avoidance of autonomous vehicles based on model predictive control,” Proc. Inst. Mech. Eng. 223 (12), 1499–1516 (2009).CrossRefGoogle Scholar
210.Peng, J. and Akella, S., “Coordinating multiple robots with kinodynamic constraints along specified paths,” Int. J. Robot. Res. 24 (4), 295–310 (2005).CrossRefGoogle Scholar
211.Peng, W., Baocang, D. and Tao, Z., “Distributed Receding Horizon Control for Nonholonomic Multi-vehicle System with Collision Avoidance,” Proceedings of the 31st Chinese Control Conference, Hefei, China (2012) pp. 6327–6332.Google Scholar
212.Petti, S. and Fraichard, T., “Partial Motion Planning Framework for Reactive Planning within Dynamic Environments,” Proceedings of the AAAI International Conference on Advanced Robotics, Barcelona, Spain (2005).Google Scholar
213.Qu, Zh., Wang, J. and Plaisted, C. E., “A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles,” IEEE Trans. Robot. 20 (6), 978–993 (2004).CrossRefGoogle Scholar
214.Raffard, R. L., Tomlin, C. J. and Boyd, S. P., “Distributed Optimization for Cooperative Agents: Application to Formation Flight,” Proceedings of the 43rd IEEE Conference on Decision and Control, Vol. 3, Paradise Island, Bahamas (2004) pp. 2453–2459.Google Scholar
215.Rakovic, S. V., Kerrigan, E. C., Kouramas, K. I. and Mayne, D. Q., “Invariant approximations of the minimal robust positively invariant set,” IEEE Trans. Autom. Control 50 (3), 406–410 (2005).CrossRefGoogle Scholar
216.Rashid, A. T., Ali, A. A., Frasca, M. and Fortuna, L., “Multi-robot collision-free navigation based on reciprocal orientation,” Robot. Auton. Syst. 60 (10), 1221–1230 (2012).CrossRefGoogle Scholar
217.Reeds, J. A. and Shepp, L. A., “Optimal paths for a car that goes both forwards and backwards,” Pac. J. Math. 145 (2), 367–393 (1990).CrossRefGoogle Scholar
218.Reif, J. and Sharir, M., “Motion planning in the presence of moving obstacles,” J. ACM 41 (4), 764–790 (1994).CrossRefGoogle Scholar
219.Ren, J., McIsaac, K. A. and Patel, R. V., “Modified newton's method applied to potential field–based navigation for mobile robots,” IEEE Trans. Robot. 22 (2), 384–391 (2006).Google Scholar
220.Ren, J., McIsaac, K. A. and Patel, R. V., “Modified Newton's method applied to potential field-based navigation for nonholonomic robots in dynamic environments,” Robotica 26 (1), 117–127 (2008).CrossRefGoogle Scholar
221.Reveliotis, S. A. and Roszkowska, E., “Conflict resolution in free-ranging multivehicle systems: A resource allocation paradigm,” IEEE Trans. Robot. 27 (2), 283–296 (2011).CrossRefGoogle Scholar
222.Richards, A. and How, J. P., “Robust Stable Model Predictive Control with Constraint Tightening,” Proceedings of the American Control Conference, Minneapolis, MN, USA (2006) pp. 1557–1562.Google Scholar
223.Richards, A. and How, J. P., “Robust variable horizon model predictive control for vehicle maneuvering,” Int. J. Robust Nonlinear Control 16 (7), 333–351 (2006).CrossRefGoogle Scholar
224.Richards, A. and How, J. P., “Robust distributed model predictive control,” Int. J. Control 80 (9), 1517–1531 (2007).CrossRefGoogle Scholar
225.Rodriguez-Seda, E. J. and Spong, M. W., “Guaranteed Safe Motion of Multiple Lagrangian Systems with Limited Actuation,” Proceedings of the 51st IEEE Conference on Decision and Control, Maui, HI, USA (2012) pp. 2773–2780.Google Scholar
226.Roussos, G., Dimarogonas, D. V. and Kyriakopoulos, K. J., “3d navigation and collision avoidance for nonholonomic aircraft-like vehicles,” Int. J. Adapt. Control Signal Process. 24 (10), 900–920 (2010).CrossRefGoogle Scholar
227.Roussos, G. P., Chaloulos, G., Kyriakopoulos, K. J. and Lygeros, J., “Control of Multiple Non-Holonomic Air Vehicles Under Wind Uncertainty Using Model Predictive Control and Decentralized Navigation Functions,” Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico (2008) pp. 1225–1230.Google Scholar
228.Rubagotti, M., Raimondo, D. M., Ferrara, A. and Magni, L., “Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems,” IEEE Trans. Autom. Control 56 (3), 556–570 (2010).CrossRefGoogle Scholar
229.Saggiani, G. M. and Teodorani, B., “Rotary wing UAV potential applications: An analytical study through a matrix method,” Aircr. Eng. Aerosp. Technol., Int. J. 76, 6–14 (2004).CrossRefGoogle Scholar
230.Samson, C., “Control of chained systems: Application to path-following and time-varying point stabilization of mobile robots,” IEEE Trans. Autom. Control 40, 64–77 (1995).CrossRefGoogle Scholar
231.Sankaranarayanan, A. and Vidyasagar, M., “Path Planning for Moving a Point Object Amidst Unknown Obstacles in a Plane: A New Algorithm and a General Theory for Algorithm Development,” Proceedings of the IEEE International Conference on Decision and Control, Brighton, UK (Dec. 1991) pp. 1111–1119.Google Scholar
232.Sankaranarayanan, A. and Vidyasagar, M., “A New Algorithm for Robot Curve-following Amidst Unknown Obstacles, and a Generalization of Maze-Searching,” Proceedings of the IEEE International Conference on Robotics and Automation, Nice, France (May 1992) pp. 2487–2494.Google Scholar
233.Sathyaraj, B. M., Jain, L. C., Finn, A. and Drake, S., “Multiple UAVs path planning algorithms: A comparative study,” Fuzzy Optim. Decis. Mak. 7 (3), 257–267 (2008).CrossRefGoogle Scholar
234.Savkin, A. V., “Coordinated collective motion of groups of autonomous mobile robots: Analysis of Vicsek's model,” IEEE Trans. Autom. Control 49 (6), 981–983 (2004).CrossRefGoogle Scholar
235.Savkin, A. V., “Analysis and synthesis of networked control systems: Topological entropy, observability, robustness and optimal control,” Automatica 42 (1), 51–62 (2006).CrossRefGoogle Scholar
236.Savkin, A. V. and Cheng, T. M., “Detectability and output feedback stabilizability of nonlinear networked control systems,” IEEE Trans. Autom. Control 52 (4), 730–735 (2007).CrossRefGoogle Scholar
237.Savkin, A. V. and Hoy, M., “Reactive and the shortest path navigation of a wheeled mobile robot in cluttered environments,” Robotica 31 (2), 323–330 (2013).CrossRefGoogle Scholar
238.Savkin, A. V., Javed, F. and Matveev, A. S., “Optimal distributed blanket coverage self-deployment of mobile wireless sensor networks,” IEEE Commun. Lett. 16 (6), 949–951 (2012).CrossRefGoogle Scholar
239.Savkin, A. V. and Teimoori, H., “Bearings-only guidance of a unicycle-like vehicle following a moving target with a smaller minimum turning radius,” IEEE Trans. Autom. Control 55 (10), 2390–2395 (2010).CrossRefGoogle Scholar
240.Savkin, A. V. and Wang, C., “A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles,” Robotica 31 (6), 993–1001 (2013).CrossRefGoogle Scholar
241.Schlegel, C., “Fast Local Obstacle Avoidance Under Kinematic and Dynamic Constraints for a Mobile Robot,” Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, Victoria, Canada (1998) pp. 594–599.Google Scholar
242.Scholte, E. and Campbell, M. E., “Robust nonlinear model predictive control with partial state information,” IEEE Trans. Control Syst. Technol. 16 (4), 636–651 (2008).CrossRefGoogle Scholar
243.Scokaert, P. O. and Mayne, D. Q., “Min–max feedback model predictive control for constrained linear systems,” IEEE Trans. Autom. Control 43 (8), 1136–1142 (1998).CrossRefGoogle Scholar
244.Seder, M., Macek, K. and Petrovic, I., “An Integrated Approach to Realtime Mobile Robot Control in Partially Known Indoor Environments,” Proceedings of the 31st Annual Conference of the IEEE Industrial Electronics Society, Raleigh, NC, USA (Nov. 2005) pp. 1785–1790Google Scholar
245.Sharma, R., Saunders, J. and Beard, R., “Reactive path planning for micro air vehicles using bearing-only measurements,” J. Intell. Robot. Syst. 65 (1), 409–416 (2012).CrossRefGoogle Scholar
246.Shi, C., Wang, Y. and Yang, J., “A local obstacle avoidance method for mobile robots in partially known environment,” Robot. Auton. Syst. 58 (5), 425–434 (2010).CrossRefGoogle Scholar
247.Shiller, Z., Gal, O. and Rimon, E., “Safe Navigation in Dynamic Environments,” In: Robot Design, Dynamics and Control (CISM Courses and Lectures) (Schiehlen, W. and Parenti-Castelli, V., eds.) Vol. 524 (Springer, Vienna, 2010) pp. 225–232.CrossRefGoogle Scholar
248.Shim, D. H., Chung, H. and Sastry, S. S., “Conflict-free navigation in unknown urban environments,” IEEE Robot. Autom. Mag. 13 (3), 27–33 (2006).CrossRefGoogle Scholar
249.Shim, D. H. and Sastry, S., “An Evasive Maneuvering Algorithm for UAVs in See-and-Avoid Situations,” Proceedings of the American Control Conference, Minneapolis, MN, USA (2007) pp. 3886–3891.Google Scholar
250.Shin, J. and Kim, H. J., “Nonlinear model predictive formation flight,” IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 39 (5), 1116–1125 (2009).CrossRefGoogle Scholar
251.Shkel, A. M. and Lumelsky, V. J., “Incorporating body dynamics into sensor-based motion planning: The maximum turn strategy,” IEEE Trans. Robot. Autom. 13 (6), 873–880 (1997).CrossRefGoogle Scholar
252.Shoenwald, D. A., “AUVs: In space, air, water, and on the ground,” IEEE Control Syst. Mag. 20 (6), 15–19 (2000).CrossRefGoogle Scholar
253.Simmons, R., “The Curvature–Velocity Method for Local Obstacle Avoidance,” IEEE International Conference on Robotics and Automation, Vol. 4, Minneapolis, MI, USA (Nov. 1996) pp. 3375–3382.Google Scholar
254.Sisbot, E. A., Marin-Urias, L. F., Alami, R. and Simeon, T., “A human aware mobile robot motion planner,” IEEE Trans. Robot. 23 (5), 874–883 (2007).CrossRefGoogle Scholar
255.Siva, E. and Maciejowski, J. M., “Robust Multiplexed MPC for Distributed Multi–Agent Systems,” Proceedings of the 18th IFAC World Congress, Milano, Italy (2011) pp. 251–256.Google Scholar
256.Skrjanc, I. and Klancar, G., “Optimal cooperative collision avoidance between multiple robots based on Bernstein–Bezier curves,” Robot. Auton. Syst. 58 (1), 1–9 (2010).CrossRefGoogle Scholar
257.Snape, J., van den Berg, J., Guy, S. J. and Manocha, D., “Independent Navigation of Multiple Mobile Robots with Hybrid Reciprocal Velocity Obstacles,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (2009) pp. 5917–5922.Google Scholar
258.Snape, J., van den Berg, J., Guy, S. J. and Manocha, D., “The hybrid reciprocal velocity obstacle,” IEEE Trans. Robot. 27 (4), 696–706 (2011).CrossRefGoogle Scholar
259.Solea, R. and Nunes, U., “Trajectory planning and sliding-mode control based trajectory-tracking for cybercars,” Integr. Comput.–Aided Eng. 14 (1), 33–47 (2007).CrossRefGoogle Scholar
260.Srinivasan, M. V., Zhang, S. W., Chahl, J. S., Barth, E. and Venkatesh, S., “How honeybees make grazing landings on flat surfaces,” Biol. Cybern. 83 (3), 171–183 (2000).CrossRefGoogle ScholarPubMed
261.Srinivasan, S., Ramamritham, K. and Kulkarni, P., “ACE, in the Hole: Adaptive Contour Estimation Using Collaborating Mobile Sensors,” Proceedings of the International Conference on Information Processing in Sensor Networks, St Louis, MO, USA (Apr. 2008) pp. 147–158.Google Scholar
262.Stachniss, C. and Burgard, W., “An Integrated Approach to Goal-Directed Obstacle Avoidance Under Dynamic Constraints for Dynamic Environments,” Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 1, Lausanne, Switzerland (2002) pp. 508–513.Google Scholar
263.Stipanovic, D. M., Hokayem, P. F., Spong, M. W. and Siljak, D. D., “Cooperative avoidance control for multiagent systems,” J. Dyn. Syst. Meas. Control 129 (5), 699–707 (2007).CrossRefGoogle Scholar
264.Summers, T. H. and Lygeros, J., “Distributed Model Predictive Consensus via the Alternating Direction Method of Multipliers,” Proceedings of the Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA (2012) pp. 79–84.Google Scholar
265.Suri, S., Vicari, E. and Widmayer, P., “Simple robots with minimal sensing: From local visibility to global geometry,” Int. J. Robot. Res. 27 (9), 1055–1067 (2008).CrossRefGoogle Scholar
266.Susca, S., Bullo, F. and Martinez, S., “Monitoring environmental boundaries with a robotic sensor network,” IEEE Trans. Control Syst. Technol. 16 (2), 288–296 (2008).CrossRefGoogle Scholar
267.Tahirovic, A. and Magnani, G., “PB/MPC Navigation Planner,” In: Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning (Springer, London, 2013) pp. 11–24.CrossRefGoogle Scholar
268.Tanner, H. G. and Boddu, A., “Multiagent navigation functions revisited,” IEEE Trans. Robot. 28 (6), 1346–1359 (2012).CrossRefGoogle Scholar
269.Tarnopolskaya, T., Fulton, N. and Maurer, H., “Synthesis of optimal bang-bang control for cooperative collision avoidance for aircraft (ships) with unequal linear speeds,” J. Optim. Theory Appl. 155 (1), 115–144 (2012).CrossRefGoogle Scholar
270.Teimoori, H. and Savkin, A. V., “A biologically inspired method for robot navigation in a cluttered environment,” Robotica 28 (5), 637–648 (2010).CrossRefGoogle Scholar
271.Teimoori, H. and Savkin, A. V., “Equiangular navigation and guidance of a wheeled mobile robot based on range-only measurements,” Robot. Auton. Syst. 58 (2), 203–215 (2010).CrossRefGoogle Scholar
272.Thrun, S., “Learning occupancy grid maps with forward sensor models,” Auton. Robots 15 (2), 111–127 (2003).CrossRefGoogle Scholar
273.Toibero, J. M., Roberti, F. and Carelli, R., “Stable contour-following control of wheeled mobile robots,” Robotica 27 (1), 1–12 (2009).CrossRefGoogle Scholar
274.Tovar, B., Murrieta-Cid, R. and LaValle, S. M., “Distance-optimal navigation in an unknown environment without sensing distances,” IEEE Trans. Robot. 23 (3), 506–518 (2007).CrossRefGoogle Scholar
275.Travis, W., Simmons, A. T. and Bevly, D. M., “Corridor Navigation with a LiDAR/INS Kalman Filter Solution,” Proceedings of the IEEE Intelligent Vehicles Symposium, Tokyo, Japan (2005) pp. 343–348.Google Scholar
276.Trevai, C., Ota, J. and Arai, T., “Multiple mobile robot surveillance in unknown environments,” Adv. Robot. 21 (7), 729–749 (2007).CrossRefGoogle Scholar
277.Tucker, V. A., “The deep fovea, sideways vision and spiral flight paths in raptors,” J. Exp. Biol. 203 (24), 3745–3754 (2001).CrossRefGoogle Scholar
278.Ulrich, I. and Borenstein, J., “VFH*: Local Obstacle Avoidance with Look–ahead Verification,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 3, San Francisco, CA, USA (2000) pp. 2505–2511.Google Scholar
279.USDoD, “Unmanned aircraft systems roadmap, 2005–2030,” Technical Report, Office of the Secretary of Defense, Washington (2005).Google Scholar
280.Utkin, V. I., Sliding Modes in Control Optimization (Springer–Verlag, Berlin, 1992).CrossRefGoogle Scholar
281.Vaccarini, M. and Longhi, S., “Formation Control of Marine Veihicles via Real-time Networked Decentralized MPC,” Proceedings of the 17th Mediterranean Conference on Control and Automation, Thessaloniki, Greece (2009) pp. 428–433.Google Scholar
282.Valbuena, L. and Tanner, H., “Hybrid potential field based control of differential drive mobile robots,” J. Intell. Robot. Syst. 68 (3–4), 307–322 (2012).CrossRefGoogle Scholar
283.van den Berg, J., Guy, S. J, Lin, M. and Manocha, D., Reciprocal n-Body Collision Avoidance, Springer Tracts in Advanced Robotics Series, Vol. 70 (Pradalier, C.et al., eds.) (Springer, Berlin, Germay, 2011) pp. 3–19.Google Scholar
284.van den Berg, J. and Overmars, M., “Planning time-minimal safe paths amidst unpredictably moving obstacles,” Int. J. Robot. Res. 27 (11–12), 1274–1294 (2008).CrossRefGoogle Scholar
285.van den Berg, J., Snape, J., Guy, S. J. and Manocha, D., “Reciprocal Collision Avoidance with Acceleration-Velocity Obstacles,” Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China (2011) pp. 3475–3482.Google Scholar
286.van den Berg, J., Wilkie, D., Guy, S. J., Niethammer, M. and Manocha, D., “Lqg-obstacles: Feedback Control with Collision Avoidance for Mobile Robots with Motion and Sensing Uncertainty,” Proceedings of the IEEE International Conference on Robotics and Automation, St. Paul, MN, USA (2012) pp. 346–353.Google Scholar
287.Victorino, A. C., Rives, P. and Borrelly, J.-J., “Safe navigation for indoor mobile robots. Part I: A sensor-based navigation framework,” Int. J. Robot. Res. 22 (12), 1005–1118 (2003).CrossRefGoogle Scholar
288.Vitus, M. P., Pradeep, V., Hoffmann, G. M., Waslander, S. L. and Tomlin, C. J., “Tunnel-MILP: Path Planning with Sequential Convex Polytopes,” Proceedings of the AIAA Guidance, Navigation, and Control Conference, Minneapolis, MN, USA (2008) pp. 1–13.Google Scholar
289.Vlassis, N. A., Sgouros, N. M., Efthivolidis, G. and Papakonstantinou, G., “Global Path Planning for Autonomous Qualitative Navigation,” Proceedings of the IEEE Conference on Tools with Artificial Intelligence, Toulouse, France (Nov. 1996) pp. 354–359.Google Scholar
290.Savkin, A. V. and Teimoori, H., “Decentralized navigation of groups of wheeled mobile robots with limited communication,” IEEE Trans. Robot. 26 (10), 1099–1104 (2010).CrossRefGoogle Scholar
291.Wakasa, Y., Arakawa, M., Tanaka, K. and Akashi, T., “Decentralized Model Predictive Control via Dual Decomposition,” Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico (2008) pp. 381–386.Google Scholar
292.Wang, C., Matveev, A. S., Savkin, A. V., Cloutz, R. and Nguyen, H.T., “A Real-time Obstacle Avoidance Strategy for Safe Autonomous Navigation of Intelligent Hospital Beds in Dynamic Uncertain Environments,” Proceedings of Australasian Conference on Robotics and Automation (Dec. 2013).CrossRefGoogle Scholar
293.Wang, C., Matveev, A. S., Savkin, A. V., Nguyen, T. N. and Nguyen, H. T., “A Collision Avoidance Strategy for Safe Autonomous Navigation of an Intelligent Electric-Powered Wheelchair in Dynamic Uncertain Environments with Moving Obstacles,” Proceedings of the European Control Conference, Zurich, Switzerland (Jul. 2013) pp. 4382–4387.Google Scholar
294.Wang, C., Savkin, A. V., Nguyen, T. N. and Nguyen, H. T., “A Novel Algorithm for Safe Navigation of Intelligent Robotic Wheelchairs for Severely Disabled People in Crowded Dynamic Environments,” Proceedings of the International Conference on Control, Automation, Robotics and Vision, Guangzhou, China (2012).Google Scholar
295.Wang, Y. and Chirikjian, G. S., “A New Potential Field Method for Robot Path Planning,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 2, San Francisco, CA, USA (2000) pp. 977–982.Google Scholar
296.Weihua, Z. and Go, T. H., “Robust decentralized formation flight control,” Int. J. Aerospace Eng. available at: www.hindawi.com/journals/ijae/2011/157590/ (2011), online.CrossRefGoogle Scholar
297.Widyotriatmo, A. and Hong, K., “Navigation function-based control of multiple wheeled vehicles,” IEEE Trans. Ind. Electron. 58 (5), 1896–1906 (2011).CrossRefGoogle Scholar
298.Wu, A. and How, J., “Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles,” Auton. Robots 32 (3), 227–242 (2012).CrossRefGoogle Scholar
299.Yang, J. M. and Kim, J. H., “Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots,” IEEE Trans. Robot. Autom. 15 (3), 578–587 (1999).CrossRefGoogle Scholar
300.Yang, K., Gan, S. and Sukkarieh, S., “An efficient path planning and control algorithm for RUAV's in unknown and cluttered environments,” J. Intell. Robot. Syst. 57 (1), 101–122 (2010).CrossRefGoogle Scholar
301.Yang, X., Alvarez, L. and Bruggemann, T., “A 3D collision avoidance strategy for UAVs in a non-cooperative environment,” J. Intell. Robot. Syst. 70 (1–4), 315–327 (2012).CrossRefGoogle Scholar
302.Yata, T., Kleeman, L. and Yuta, S., “Wall Following Using Angle Information Measured by a Single Ultrasonic Transducer,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 2, Leuven, Belgium (1998) pp. 1590–1596.Google Scholar
303.Yoon, Y., Shin, J., Kim, H. J., Park, Y. and Sastry, S., “Model-predictive active steering and obstacle avoidance for autonomous ground vehicles,” Control Eng. Pract. 17 (7), 741–750 (2009).CrossRefGoogle Scholar
304.Yu, H., Sharma, R., Beard, R. W. and Taylor, C. N., “Observability-Based Local Path Planning and Collision Avoidance for Micro Air Vehicles using Bearing-only Measurements,” Proceedings of the American Control Conference, San Francisco, CA, USA (2011) pp. 4649–4654.Google Scholar
305.Zhang, Ch., Arnold, D., Ghods, N., Siranosian, A. and Krstic, M., “Source seeking with non–holonomic unicycle without position measurement and with tuning of forward velocity,” Syst. Control Lett. 56 (3), 245–252 (2007).CrossRefGoogle Scholar
306.Zhang, F., Fratantoni, D. M., Paley, D. A., Lund, J. M. and Leonard, N. E., “Control of coordinated patterns for ocean sampling,” Int. J. Control 80 (7), 1186–1199 (2007).CrossRefGoogle Scholar
307.Zhang, F., Justh, E. W. and Krishnaprasad, P. S., “Boundary Following Using Gyroscopic Control,” Proceedings of the 43rd IEEE Conference on Decision and Control, Vol. 5, Paradise Island, Bahamas (2004) pp. 5204–5209.Google Scholar
308.Zhang, F. and Leonard, N. E., “Cooperative control and filtering for cooperative exploration,” IEEE Trans. Autom. Control 55 (3), 650–663 (2010).CrossRefGoogle Scholar
309.Zheng, C., Li, L., Xu, F., Sun, F. and Ding, M., “Evolutionary route planner for unmanned air vehicles,” IEEE Trans. Robot. 21 (4), 609–620 (2005).CrossRefGoogle Scholar
310.Zhipu, J. and Bertozzi, A. L., “Environmental Boundary Tracking and Estimation Using Multiple Autonomous Vehicles,” Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, LU, USA (Dec. 2007) pp. 4918–4923.Google Scholar
311.Zhu, Y. and Ozguner, U., “Constrained Model Predictive Control for Nonholonomic Vehicle Regulation Problem,” Proceedings of the 17th IFAC World Congress, Seoul, South Korea (2008) pp. 9552–9557.Google Scholar
312.Zhu, Y., Zhang, T. and Song, J., “An Improved Wall Following Method for Escaping from Local Minimum in Artificial Potential Field Based Path Planning,” Proceedings of the 48th IEEE Conference on Decision and Control and the 28th Chinese Control Conference, Shanghai, China (2009) pp. 6017–6022.Google Scholar
313.Zhu, Y., Zhang, T., Song, J. and Li, X., “A new hybrid navigation algorithm for mobile robots in environments with incomplete knowledge,” Knowl.–Based Syst. 27, 302–313 (2012).CrossRefGoogle Scholar
314.Ziebart, B. D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J. A., Hebert, M., Dey, A. K. and Srinivasa, S., “Planning–Based Prediction for Pedestrians,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St Louis, MO, USA (2009) pp. 3931–3936.Google Scholar