Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-08T16:24:27.172Z Has data issue: false hasContentIssue false

Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

Published online by Cambridge University Press:  31 August 2018

M. Hassan Tanveer*
Affiliation:
Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Via Opera Pia 13, 16145, Italy. Emails: [email protected], [email protected]
Carmine T. Recchiuto
Affiliation:
Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Via Opera Pia 13, 16145, Italy. Emails: [email protected], [email protected]
Antonio Sgorbissa
Affiliation:
Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Via Opera Pia 13, 16145, Italy. Emails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

The article presents the experimental evaluation of an integrated approach for path following and obstacle avoidance, implemented on wheeled robots. Wheeled robots are widely used in many different contexts, and they are usually required to operate in partial or total autonomy: in a wide range of situations, having the capability to follow a predetermined path and avoiding unexpected obstacles is extremely relevant. The basic requirement for an appropriate collision avoidance strategy is to sense or detect obstacles and make proper decisions when the obstacles are nearby. According to this rationale, the approach is based on the definition of the path to be followed as a curve on the plane expressed in its implicit form f(x, y) = 0, which is fed to a feedback controller for path following. Obstacles are modeled through Gaussian functions that modify the original function, generating a resulting safe path which – once again – is a curve on the plane expressed as f′(x, y) = 0: the deformed path can be fed to the same feedback controller, thus guaranteeing convergence to the path while avoiding all obstacles. The features and performance of the proposed algorithm are confirmed by experiments in a crowded area with multiple unicycle-like robots and moving persons.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

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

1. 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), 515524 (2011).Google Scholar
2. Bonin-Font, F., Ortiz, A. and Oliver, G., “Visual navigation for mobile robots: A survey,” J. Intell. Robot. Syst. 53 (3), 263 (2008).Google Scholar
3. Mosteo, A. R., Montijano, E. and Tardioli, D., “Optimal role and position assignment in multi-robot freely reachable formations,” Automatica 81, 305313 (2017).Google Scholar
4. Oyler, D. W., Kabamba, P. T. and Girard, A. R., “Pursuit–evasion games in the presence of obstacles,” Automatica 65, 111 (2016).Google Scholar
5. Doosthoseini, A., Nielsen, C., “Coordinated path following for unicycles: A nested invariant sets approach,” Automatica 60, 1729 (2015).Google Scholar
6. Kantaros, Y. and Zavlanos, M. M., “Distributed communication-aware coverage control by mobile sensor networks,” Automatica 63, 209220 (2016).Google Scholar
7. Li, X., Yang, H., Wang, J. and Sun, D., “Design of a robust unified controller for cell manipulation with a robot-aided optical tweezers system,” Automatica 55, 279286 (2015).Google Scholar
8. Li, X., Sun, D. and Yang, J., “A bounded controller for multirobot navigation while maintaining network connectivity in the presence of obstacles,” Automatica 49 (1), 285292 (2013).Google Scholar
9. Matveev, A. S., Hoy, M. and Savkin, A. V., “A method for reactive navigation of nonholonomic under-actuated robots in maze-like environments,” Automatica 49 (5), 12681274 (2013).Google Scholar
10. Savkin, A. V. and Wang, C., “Seeking a path through the crowd: Robot navigation in unknown dynamic environments with moving obstacles based on an integrated environment representation,” Robot. Auton. Syst. 62 (10), 15681580 (2014).Google Scholar
11. Xiao-Qing, L., Yao-Nan, W. and Jian-Xu, M., “Nonlinear control for multi-agent formations with delays in noisy environments,” Acta Autom. Sin. 40 (12), 29592967 (2014).Google Scholar
12. Su, Y., “Leader-following rendezvous with connectivity preservation and disturbance rejection via internal model approach,” Automatica 57, 203212 (2015).Google Scholar
13. Sun, X. and Cassandras, C. G., “Optimal dynamic formation control of multi-agent systems in constrained environments,” Automatica 73, 169179 (2016).Google Scholar
14. Tian-Tian, Y., Zhi-Yuan, L., Hong, C. and Run, P., “Formation control and obstacle avoidance for multiple mobile robots,” Acta Autom. Sin. 34 (5), 588593 (2008).Google Scholar
15. Malisoff, M., Sizemore, R. and Zhang, F., “Adaptive planar curve tracking control and robustness analysis under state constraints and unknown curvature,” Automatica 75, 133143 (2017).Google Scholar
16. Morro, A., Sgorbissa, A. and Zaccaria, R., “Path following for unicycle robots with an arbitrary path curvature,” IEEE Trans. Robot. 27 (5), 10161023 (2011).Google Scholar
17. Michalek, M., “A highly scalable path-following controller for n-trailers with off-axle hitching,” Control Eng. Pract. 29, 6173 (2014).Google Scholar
18. Sgorbissa, A. and Zaccaria, R., “3d Path Following with No Bounds on the Path Curvature through Surface Intersection,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 2010.Google Scholar
19. Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 9098 (1986).Google Scholar
20. Borenstein, J. and Koren, Y., “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Trans. Robot. Autom. 7 (3), 278288 (1991).Google Scholar
21. 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 (06), 9931001 (2013).Google Scholar
22. Nguyen, P. D. H., Recchiuto, C. T. and Sgorbissa, A., “Real-time path generation and obstacle avoidance for multirotors: A novel approach,” J. Intell. Robot. Syst. 89 (1), 2749 (2018).Google Scholar
23. Possieri, C. and Teel, A. R., “Lq Optimal Control for a Class of Hybrid Systems,” Proceedings of the IEEE 55th Conference on Decision and Control (CDC), IEEE (2016) pp. 604–609.Google Scholar
24. 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 (03), 463497 (2015).Google Scholar
25. Minguez, J., Lamiraux, F. and Laumond, J.-P., “Motion Planning and Obstacle Avoidance,” In: Springer Handbook of Robotics (Springer, 2016) pp. 1177–1202.Google Scholar
26. Savkin, A. V., Matveev, A. S., Hoy, M. and Wang, C., Safe Robot Navigation Among Moving and Steady Obstacles (Butterworth-Heinemann, 2015).Google Scholar
27. Garone, E. et al., “Reference and command governors for systems with constraints: A survey on theory and applications,” Automatica 75, 306328 (2017).Google Scholar
28. Atınç, G. M., Stipanović, D. M. and Voulgaris, P. G., “Supervised coverage control of multi-agent systems,” Automatica 50 (11), 29362942 (2014).Google Scholar
29. Zhang, K., Sprinkle, J. and Sanfelice, R. G., “A Hybrid Model Predictive Controller for Path Planning and Path Following,” Proceedings of the ACM/IEEE 6th International Conference on Cyber-Physical Systems, ACM (2015) pp. 139–148.Google Scholar
30. Brüggemann, S., Possieri, C., Poveda, J. I. and Teel, A. R., “Robust Constrained Model Predictive Control with Persistent Model Adaptation,” Proceedings of the IEEE 55th Conference on Decision and Control (CDC), IEEE (2016) pp. 2364–2369.Google Scholar
31. Fagiano, L. and Teel, A. R., “Model predictive control with generalized terminal state constraint,” IFAC Proc. 45 (17), 299304 (2012).Google Scholar
32. Yu-Geng, X., De-Wei, L. and Shu, L., “Model predictive control⣠status and challenges,” Acta Autom. Sin. 39 (3), 222236 (2013).Google Scholar
33. Liberzon, D., Calculus of Variations and Optimal Control Theory: A Concise Introduction (Princeton University Press, Princeton, New Jersey, 2012).Google Scholar
34. Pin, G., Raimondo, D. M., Magni, L. and Parisini, T., “Robust model predictive control of nonlinear systems with bounded and state-dependent uncertainties,” IEEE Trans. Autom. Control 54 (7), 16811687 (2009).Google Scholar
35. Goodwin, G. C., Middleton, R. H., Seron, M. M. and Campos, B., “Application of nonlinear model predictive control to an industrial induction heating furnace,” Annu. Rev. Control 37 (2), 271277 (2013).Google Scholar
36. Rana, M. S., Pota, H. R. and Petersen, I. R., “The design of model predictive control for an afm and its impact on piezo nonlinearities,” Eur. J. Control 20 (4), 188198 (2014).Google Scholar
37. Kouzoupis, D., Zanelli, A., Peyrl, H. and Ferreau, H., “Towards Proper Assessment of qp Algorithms for Embedded Model Predictive Control,” Proceedings of the Control Conference (ECC), 2015 European, IEEE (2015) pp. 2609–2616.Google Scholar
38. Kim, W., Kim, D., Yi, K. and Kim, H. J., “Development of a path-tracking control system based on model predictive control using infrastructure sensors,” Veh. Syst. Dyn. 50 (6), 10011023 (2012).Google Scholar
39. Parys, R. V. and Pipeleers, G., “Distributed MPC for multi-vehicle systems moving in formation,” Robot. Autonom. Syst. 97, 144152 (2017). https://doi.org/10.1016/j.robot.2017.08.009Google Scholar
40. Franzè, G. and Lucia, W., “An obstacle avoidance model predictive control scheme for mobile robots subject to nonholonomic constraints: A sum-of-squares approach,” J. Franklin Inst. 352 (6), 23582380 (2015).Google Scholar
41. Nascimento, T. P., Conceicao, A. G. and Moreira, A. P., “Multi-robot systems formation control with obstacle avoidance,” IFAC Proc. Volumes 47 (3), 57035708 (2014). https://doi.org/10.3182/20140824-6-za-1003.01848Google Scholar
42. Bououden, S., Chadli, M. and Karimi, H. R., “An ant colony optimization-based fuzzy predictive control approach for nonlinear processes,” Inform. Sci. 299, 143158 (2015).Google Scholar
43. Fiorini, P. and Shiller, Z., “Motion Planning in Dynamic Environments using the Relative Velocity Paradigm,” Proceedings IEEE International Conference on Robotics and Automation, IEEE (1993) pp. 560–565.Google Scholar
44. Kuwata, Y., Wolf, M. T., Zarzhitsky, D. and Huntsberger, T. L., “Safe maritime autonomous navigation with colregs, using velocity obstacles,” IEEE J. Ocean. Eng. 39 (1), 110119 (2014).Google Scholar
45. Large, F., Laugier, C. and Shiller, Z., “Navigation among moving obstacles using the NLVO: Principles and applications to intelligent vehicles,” Auton. Robots 19 (2), 159171 (2005).Google Scholar
46. 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 (06), 9931001 (2013). https://doi.org/10.1017/s0263574713000313Google Scholar
47. Montiel, O., Orozco-Rosas, U. and Sepúlveda, R., “Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles,” Expert Syst. Appl. 42 (12), 51775191 (2015).Google Scholar
48. Mujahed, M., Fischer, D. and Mertsching, B., “Admissible gap navigation: A new collision avoidance approach,” Robot. Auton. Syst. 103, 93110 (2018). https://doi.org/10.1016/j.robot.2018.02.008Google Scholar
49. Xu, Z., Hess, R. and Schilling, K., “Constraints of potential field for obstacle avoidance on car-like mobile robots,” IFAC Proc. Volumes 45 (4), 169175 (2012). https://doi.org/10.3182/20120403-3-de-3010.00077Google Scholar
50. Korayem, M. and Nekoo, S., “The SDRE control of mobile base cooperative manipulators: Collision free path planning and moving obstacle avoidance,” Robot. Auton. Syst. 86, 86105 (2016). https://doi.org/10.1016/j.robot.2016.09.003Google Scholar
51. Sgorbissa, A., “Integrated robot planning, obstacle avoidance, and path following in 2D and 3D: Ground, aerial, and underwater vehicles” (2017) –doi:10.13140/rg.2.2.14838.80969.Google Scholar
52. Al-Jarrah, R., Al-Jarrah, M. and Roth, H., “A novel edge detection algorithm for mobile robot path planning,” J. Robot. 2018, 112 (2018).Google Scholar
53. Deepu, R., Honnaraju, B. and Murali, S., “Path generation for robot navigation using a single camera,” Proc. Comput. Sci. 46, 14251432 (2015).Google Scholar
54. Falconi, R., Sabattini, L., Secchi, C., Fantuzzi, C. and Melchiorri, C., “Edge-weighted consensus-based formation control strategy with collision avoidance,” Robotica 33 (2), 332347 (2015).Google Scholar
55. Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag. 4 (1), 2333 (1997).Google Scholar
56. Arras, K. O., Persson, J., Tomatis, N. and Siegwart, R., “Real-Time Obstacle Avoidance for Polygonal Robots with a Reduced Dynamic Window,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA'02, Vol. 3, IEEE (2002) pp. 3050–3055.Google Scholar
57. Ogren, P. and Leonard, N. E., “A convergent dynamic window approach to obstacle avoidance,” IEEE Trans. Robot. 21 (2), 188195 (2005).Google Scholar
58. Damas, B. and Santos-Victor, J., “Avoiding Moving Obstacles: The Forbidden Velocity Map,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, IEEE (2009) pp. 4393–4398.Google Scholar
59. Lapierre, L., Zapata, R. and Lepinay, P., “Simulatneous Path Following and Obstacle Avoidance Control of a Unicycle-Type Robot,” Proceedings of the IEEE International Conference on Robotics and Automation, IEEE (2007) pp. 2617–2622.Google Scholar
60. Aicardi, M., Casalino, G., Bicchi, A. and Balestrino, A., “Closed loop steering of unicycle like vehicles via Lyapunov techniques,” IEEE Robot. Autom. Mag. 2 (1), 2735 (1995).Google Scholar
61. Soetanto, D., Lapierre, L. and Pascoal, A., “Adaptive, Non-Singular Path-Following Control of Dynamic Wheeled Robots,” Proceedings of the 42nd IEEE Conference on Decision and Control, 2003, Vol. 2, IEEE (2003) pp. 1765–1770.Google Scholar
62. Miao, H. and Tian, Y.-C., “Dynamic robot path planning using an enhanced simulated annealing approach,” Appl. Math. Comput. 222, 420437 (2013). https://doi.org/10.1016/j.amc.2013.07.022Google Scholar
63. Mohammadi, A., Rahimi, M. and Suratgar, A. A., “A New Path Planning and Obstacle Avoidance Algorithm in Dynamic Environment,” Proceedings of the 22nd Iranian Conference on Electrical Engineering (ICEE), IEEE (2014).Google Scholar
64. Mylvaganam, T. and Sassano, M., “Autonomous collision avoidance for wheeled mobile robots using a differential game approach,” Eur. J. Control 40 5361 (2018). https://doi.org/10.1016/j.ejcon.2017.11.005Google Scholar
65. Hsien-I, L., “2d-span resampling of bi-RRT in dynamic path planning,” Int. J. Autom. Smart Technol. 4 (4), 3948 (2015). https://doi.org/10.5875/ausmt.v5i1.837Google Scholar
66. Moon, C.-b. and Chung, W., “Kinodynamic planner dual-tree RRT (dt-rrt) for two-wheeled mobile robots using the rapidly exploring random tree,” IEEE Trans. Ind. Electron. 62 (2), 10801090 (2015).Google Scholar
67. Chwa, D., “Robust distance-based tracking control of wheeled mobile robots using vision sensors in the presence of kinematic disturbances,” IEEE Trans. Ind. Electron. 63 (10), 61726183 (2016). doi:10.1109/TIE.2016.2590378.Google Scholar