Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-23T15:47:59.907Z Has data issue: false hasContentIssue false

Sensor-based Navigation of Omnidirectional Wheeled Robots Dealing with both Collisions and Occlusions

Published online by Cambridge University Press:  11 July 2019

Abdellah Khelloufi*
Affiliation:
Center for Development of Advanced Technologies CDTA, 20 Aout 1956 City, Baba Hassen Algiers, Algeria Faculty of Electronics and Computer Science, USTHB, BP32 EL-ALIA, 16111 Bab Ezzouar Algiers, Algeria. E-mail: [email protected] LIRMM, Université de Montpellier, CNRS, Montpellier, France. E-mails: [email protected], [email protected]
Nouara Achour
Affiliation:
Faculty of Electronics and Computer Science, USTHB, BP32 EL-ALIA, 16111 Bab Ezzouar Algiers, Algeria. E-mail: [email protected]
Robin Passama
Affiliation:
LIRMM, Université de Montpellier, CNRS, Montpellier, France. E-mails: [email protected], [email protected]
Andrea Cherubini
Affiliation:
LIRMM, Université de Montpellier, CNRS, Montpellier, France. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

Navigation tasks are often subject to several constraints that can be related to the sensors (visibility) or come from the environment (obstacles). In this paper, we propose a framework for autonomous omnidirectional wheeled robots that takes into account both collision and occlusion risk, during sensor-based navigation. The task consists in driving the robot towards a visual target in the presence of static and moving obstacles. The target is acquired by fixed – limited field of view – on-board cameras, while the surrounding obstacles are detected by lidar scanners. To perform the task, the robot has not only to keep the target in view while avoiding the obstacles, but also to predict its location in the case of occlusion. The effectiveness of our approach is validated through several experiments.

Type
Articles
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

LaValle, S. M., Planning Algorithms (Cambridge University Press, Cambridge, UK, 2006).CrossRefGoogle Scholar
Mac, T. T., Copot, C., Tran, D. T. and De Keyser, R., “Heuristic approaches in robot path planning: A survey,Robot. Auton. Syst. 86, 1328 (2016).CrossRefGoogle Scholar
Sprunk, C., Lau, B., Pfaff, P. and Burgard, W., “An accurate and efficient navigation system for omnidirectional robots in industrial environments,” Auton. Robots. 41(2), 473493 (2017).CrossRefGoogle Scholar
Khatib, O., “Real-time Obstacle Avoidance for Manipulators and Mobile Robots,” IEEE International Conference on Robotics and Automation, ICRA, St Louis, MO, USA (1985) pp. 500505.Google Scholar
Cherubini, A. and Chaumette, F., “Visual Navigation with a Time-independent Varying Reference,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, St Louis, MO, USA (2009) pp. 59685973.Google Scholar
Borenstein, J. and Koren, Y., “The Vector Field Histogram - Fast obstacle avoidance for mobile robots,IEEE Trans. Robot. Autom. 7(3), 278288 (1991).CrossRefGoogle Scholar
Fox, D., Burgard, W. and Thrun, S., “The Dynamic Window approach to obstacle avoidance,IEEE Robot. Autom. Mag. 4(1), 2333 (1997).CrossRefGoogle Scholar
Minguez, J., “The Obstacle-Restriction Method for Robot Obstacle Avoidance in Difficult Environments,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Edmonton, Alta, Canada (2005) pp. 22842290.Google Scholar
Mujahad, M., Fischer, D., Mertsching, B. and Jaddu, H., “Closest Gap Based (CG) Reactive Obstacle Avoidance Navigation for Highly Cluttered Environments,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Taipei, Taiwan (2010) pp. 18051812.Google Scholar
Hoy, M., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey,Robotica 33, 463497 (2015).CrossRefGoogle Scholar
Fiorini, P. and Shiller, Z., “Motion planning in dynamic environments using velocity obstacles,Int. J. Robot. Res. 17(7), 760772 (1998).CrossRefGoogle Scholar
Large, F., Lauger, C. and Shiller, Z., “Navigation among moving obstacles using the NLVO: Principles and applications to intelligent vehicles,Auton. Robots 19(2), 159171 (2005).CrossRefGoogle Scholar
Zhang, W., Wei, S., Teng, Y., Zhang, J., Wang, X. and Yan, Z., “Dynamic obstacle avoidance for unmanned underwater vehicles based on an improved velocity obstacle method,Sensors 17(12), 2742 (2017).CrossRefGoogle Scholar
van den Berg, J., Ferguson, D. and Kuffner, J., “Anytime path planning and replanning in dynamic environments,” IEEE International Conference on Robotics and Automation, ICRA, Orlando, FL, USA (2006) pp. 23662371.Google Scholar
Du Toit, N. E. and Burdick, J. W., “Robot motion planning in dynamic, uncertain environments,IEEE Trans. Robot . 28(1), 101115 (2012).CrossRefGoogle Scholar
Fulgenzi, C., Spalanzani, A. and Laugier, C., “Probabilistic motion planning among moving obstacles following typical motion patterns,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, St Louis, MO, USA (2009) pp. 40274033.Google Scholar
Foka, A. and Trahanias, P., “Probabilistic autonomous robot navigation in dynamic environments with human motion prediction,Int. J. Soc. Robot. 2(1), 7994 (2010).CrossRefGoogle Scholar
Kim, B. and Pineau, J., “Socially adaptive path planning in human environments using inverse reinforcement learning,Int. J. Soc. Robot. 8(1), 5166 (2015).CrossRefGoogle Scholar
Chen, Y. F., Everett, M., Liu, M. and How, J. P., “Socially aware motion planning with deep reinforcement learning,” ‘IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Vancouver, BC,Canada (2017) pp. 13431350.Google Scholar
Ge, S. S. and Cui, Y. J., “Dynamic motion planning for mobile robots using potential field method,Auton. Robots 13(3), 207222 (2002).CrossRefGoogle Scholar
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), 117127 (2008).CrossRefGoogle Scholar
Xin, L., Yin, Y. and Lin, C. J., “A new potential field method for mobile robot path planning in the dynamic environment,Asian J. Control 11(2), 214225 (2009).Google Scholar
Zhang, Q., Yue, S., Yin, Q. and Zha, Y., “Dynamic obstacle-avoiding path planning for robots based on modified potential field method,Intell. Comput. Theor. Technol. 7996, 332342 (2013).CrossRefGoogle Scholar
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), 9931001 (2013).CrossRefGoogle Scholar
Montiel, O., Orozco, R. U. and Sepulveda, R., “Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles,Expert Syst. Appl . 42, 51775191 (2015).CrossRefGoogle Scholar
Chaumette, F. and Hutchinson, S., “Visual servo control, Part I: Basic approaches,IEEE Robot. Autom. Mag. 13(4), 8290 (2006).CrossRefGoogle Scholar
Chaumette, F. and Hutchinson, S., “Visual servo control, Part II : Advanced approaches,IEEE Robot. Autom. Mag. 14(1), 109118 (2007).CrossRefGoogle Scholar
Folio, D. and Cadenat, V., “A redundancy-based scheme to perform safe vision-based tasks amidst obstacles,” IEEE International Conference on Robotics and Biomimetics, Kunming, China (2006) pp. 1318.Google Scholar
Futterlieb, M., Cadenat, V. and Sentenac, T., “A Navigational Framework Combining Visual Servoing and Spiral Obstacle Avoidance Techniques,International Conference on Informatics in Control, Automation and Robotics, Vienna, Austria (2014) pp. 5764.CrossRefGoogle Scholar
Cherubini, A. and Chaumette, F.. “Visual navigation of a mobile robot with laser-based collision avoidance,Int. J. Robot. Res. 32(2), 189205 (2013).CrossRefGoogle Scholar
Cherubini, A., Spindler, F. and Chaumette, F., “A new tentacles-based technique for avoiding obstacles during visual navigation,” IEEE International Conference on Robotics and Automation, ICRA, St Paul, MN, USA (2012) pp. 48504855.Google Scholar
Cherubini, A., Grechanichenko, B., Spindler, F. and Chaumette, F., “Avoiding moving obstacles during Visual Navigation,” IEEE International Conference on Robotics and Automation, ICRA, Karlsruhe, Germany (2013) pp. 30693074.Google Scholar
Cherubini, A., Spindler, F. and Chaumette, F.. “Autonomous visual navigation and laser-based moving obstacle avoidance,IEEE Trans. Int. Trans. Syst. 15(5), 21012110 (2014).CrossRefGoogle Scholar
Mezouar, Y. and Chaumette, F., “Avoiding self-occlusions and preserving visibility by path planning in the image,Robot. Auton. Syst. 41(2), 7787 (2002).Google Scholar
Chesi, G. and Hashimoto, K., “Keeping features in the field of view in eye-in-hand visual servoing: a switching approach,IEEE Trans. Robot . 20(5), 908914 (2004).CrossRefGoogle Scholar
Remazeilles, A., Mansard, N. and Chaumette, F., “A Qualitative Visual Servoing to ensure the Visibility Constraint,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Beijing, China (2006) pp. 42974303.Google Scholar
Kazemi, M., Gupta, K. and Mehrandezh, M., “Randomized kinodynamic planning for robust visual servoing,IEEE Trans. Robot. 29(5), 11971211 (2013).CrossRefGoogle Scholar
Kermorgant, O. and Chaumette, F., “Dealing with constraints in sensor- based robot control,IEEE Trans. Robot . 30(1), 244257 (2014).CrossRefGoogle Scholar
Cazy, N., Wieber, P.-B., Giordano, P. R. and Chaumette, F., “Visual Servoing when Visual Information is Missing: Experimental Comparison of Visual Feature Prediction Schemes,” IEEE International Conference on Robotics and Automation, ICRA, Seattle, WA, USA (2015) pp. 60316036.Google Scholar
Bhattacharya, S., Murrieta-Cid, R. and Hutchinson, S., “Optimal paths for landmark-based navigation by differential-drive robots with field-of-view constraints,IEEE Trans. Robot . 23(1), 4759 (2007).CrossRefGoogle Scholar
Salaris, P., Fontanelli, D., Pallottino, L. and Bicchi, A., “Shortest paths for a robot with nonholonomic and field-of view constraints,IEEE Trans. Robot . 26(2), 269280 (2010).CrossRefGoogle Scholar
Hayet, J., Esteves, C. and Murrieta-Cid, R., “A Motion Planner for Maintaining Landmark Visibility with a Differential Drive Robot,” In: Algorithmic Foundations of Robotics VIII (Springer, Berlin, Germany, 2009).Google Scholar
Hayet, J.-B., Carlos, H., Esteves, C. and Murrieta-Cid, R., “Motion planning for maintaining landmarks visibility with a differential drive robot,Robot. Auton. Syst. 62(4), 456473 (2014).CrossRefGoogle Scholar
Panagou, D. and Kumar, V., “Cooperative visibility maintenance for leader-follower formations in obstacle environments,IEEE Trans. Robot . 30(4), 831844 (2014).CrossRefGoogle Scholar
Lopez-Nicolas, G., Gans, N. R., Bhattacharya, S., Sagüés, C., Guerrero, J. J. and Hutchinson, S., “An optimal homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints,IEEE Trans. Syst., Man, Cybern. B, Cybern. 40(4), 11151127 (2010).CrossRefGoogle Scholar
Folio, D. and Cadenat, V., “A sensor-based controller able to treat total image loss and to guarantee noncollision during a vision-based navigation task,” In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Nice, France (2008) pp. 30523057.Google Scholar
Khelloufi, A., Achour, N., Passama, R. and Cherubini, A., “Tentacle-based moving obstacle avoidance for omnidirectional robots with visibility constraints,” IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Vancouver, BC, Canada (2017) pp. 13311336.Google Scholar
de Wit, C. Canudas, Siciliano, B., Bastin, G., Theory of Robot Control (Springer-Verlag, Berlin, 1996).CrossRefGoogle Scholar
Cherubini, A., Crosnier, A., Fraisse, P., Navarro, B., Passama, R. and Sorour, M., “Research on cobotics at the LIRMM IDH group,” ICRA (2017) Workshop IC3 – Industry of the future, Singapore, Singapore (2017).Google Scholar

Khelloufi et al. supplementary material

Khelloufi et al. supplementary material 1

Download Khelloufi et al. supplementary material(Video)
Video 52.7 MB