access icon free Vehicle platoon and obstacle avoidance: a reactive agent approach

Reactive multi-agent systems are increasingly applied to problem solving, modelling and simulation. Among the benefits of this approach, there is the capability to solve complex problems, whereas maintaining functional and conceptual simplicity of involved entities. Many examples have shown that approaches based on multi-agent systems are effective in solving complex problems such as life simulation and multi-robot cooperation for instance. Side-by-side vehicle platoon systems, which are studied in this article, can be adopted as a representative example of this class of applications. Based on preceding works, this study presents a local platoon control approach with obstacle avoidance. The proposed vehicle decision-making process is considered as a multi-agent system, the agents of which make collectively the best decision considering the perceived constraints and the preceding vehicle position and speed.

Inspec keywords: mobile robots; decision making; multi-robot systems; velocity control; position control; collision avoidance; multi-agent systems

Other keywords: obstacle avoidance; conceptual simplicity; functional simplicity; vehicle speed; local platoon control approach; vehicle position; problem solving; reactive multiagent systems; multirobot cooperation; vehicle decision-making process; side-by-side vehicle platoon systems

Subjects: Mobile robots; Velocity, acceleration and rotation control; Spatial variables control

References

    1. 1)
      • 3. Simonin, O., Gechter, F.: ‘An environment-based methodology to design reactive multi-agent systems for problem solving’, In Weyns, D., Van Dyke Parunak, H., Michel, F. (Eds.): ‘Environment for Multiagent Systems II (revised and selected papers of E4MAS 2005)’ (Springer-Verlag Berlin Heidelberg, 2006), (LNAI3830/2006), pp. 3249.
    2. 2)
      • 2. Weyns, D., Parunak, V., Michel, F., Holvoet, T., Ferber, J.: ‘Environments for multiagent systems, state of the art and research challenges’. Post-proceedings of the first Int. Workshop on Environments for Multiagent Systems, 2005(LNAI3374).
    3. 3)
      • 17. Reeds, J.A., Shepp, L.A.: Optimal paths for a car that goes both forwards and backwards’, Pacific J. Math., 1990, 145, (2), pp. 367393 (doi: 10.2140/pjm.1990.145.367).
    4. 4)
      • 13. Das, A.K., Fierro, R., Kumar, V., Ostrowski, J.P., Spletzer, J., Taylor, C.J.: ‘A vision-based formation control framework’. IEEE Trans. Robot. Autom., 2002, 18, (5), pp. 813825 (doi: 10.1109/TRA.2002.803463).
    5. 5)
      • 11. Lawton, J.R.T., Beard, R.W., Young, B.J.: ‘A decentralized approach to formation maneuvers’, IEEE Trans. Robot. Autom., 2003, 19, (6), pp. 933941 (doi: 10.1109/TRA.2003.819598).
    6. 6)
      • 14. Vidal, R., Shakernia, O., Sastry, S.: ‘Following the flock [formation control]’, IEEE Robot. Autom. Mag., 2004, 11, (4), pp. 1420 (doi: 10.1109/MRA.2004.1371604).
    7. 7)
      • 18. Kavraki, L., Svestka, P., Latombe, J.c., Over-mars, M.: ‘Probabilistic roadmaps for path planning in high-dimensional configuration spaces’. In IEEE Int. Conf. Robot. Autom., 1996, pp. 566580.
    8. 8)
      • 21. Contet, J.M., Gechter, F., Gruer, P., Koukam, A.: ‘An approach to compositional verification of reactive multiagent systems’. Working Notes of the Twenty-Fourth AAAI Conf. Artificial Intelligence (AAAI), Workshop on Model Checking and Artificial Intelligence, Atlanta, Georgia, USA, 11–12 July 2010, 2010.
    9. 9)
      • 7. Chapelle, J., Simonin, O., Ferber, J.: ‘How situated agents can learn to cooperate by monitoring their neighbors satisfaction’. ECAI, Lyon, August 2002, pp. 6872, 2002.
    10. 10)
      • 19. Blanc, G., Mezouar, Y., Martinet, P.: ‘A path planning strategy for obstacle avoidance’. Proc. Third Int. Conf. Informatics in Control, Automation and Robotics (ICINCO06), 2006.
    11. 11)
      • 9. Defoort, M., Floquet, T., Kokosy, A., Perruquetti, W.: ‘Sliding-mode formation control for cooperative autonomous mobile robots’, IEEE Trans. Ind. Electron., 2008, 55, (11), pp. 39443953 (doi: 10.1109/TIE.2008.2002717).
    12. 12)
      • 16. Bom, J., Martinet, P., Thuilot, B.: Autonomous navigation and platooning using a sensory memory’. Int. IEEE Conf. Intelligent Robots and Systems (IROS'06), Beijing, China, October 2006, 2006.
    13. 13)
      • 3. Simonin, O., Gechter, F.: ‘An environment-based methodology to design reactive multi-agent systems for problem solving’, In Weyns, D., Van Dyke Parunak, H., Michel, F. (Eds.): ‘Environment for Multiagent Systems II (revised and selected papers of E4MAS 2005)’ (Springer-Verlag Berlin Heidelberg, 2006), (LNAI3830/2006), pp. 3249.
    14. 14)
      • 6. Gechter, F., Chevrier, V., Charpillet, F.: ‘A reactive agent-based problem-solving model: application to localization and tracking’, ACM Trans. Autonom. Adaptive Syst., 2006, 1, (2), pp. 189222 (doi: 10.1145/1186778.1186781).
    15. 15)
      • 12. Ogren, P., Egerstedt, M., Hu, X.: ‘A control Lyapunov function approach to multiagent coordination’. IEEE Trans. Robot. Autom., 2002, 18, (5), pp. 847851 (doi: 10.1109/TRA.2002.804500).
    16. 16)
      • 8. Desai, J.P., Ostrowski, J., Kumar, V.: ‘Controlling formations of multiple mobile robots’. In Proc. IEEE Int. Conf. Robotics and Automation, 1998, pp. 28642869.
    17. 17)
      • 20. Gechter, F., Contet, J.M., Gruer, P., Koukam, A.: ‘Car-driving assistance using organization measurement of reactive multi-agent system’. Int. Conf. Computational Science 2010 (ICCS 2010), Amsterdam, 31 May–2 June 2010, 2010.
    18. 18)
      • 5. Brueckner, S.: ‘Return from the ant: synthetic eco-systems for manufacturing control’. Thesis at Humboldt University Berlin, Department of Computer Science, 2000, 2000.
    19. 19)
      • 10. Balch, T., Arkin, R.C.: ‘Behavior-based formation control for multirobot teams’, IEEE Trans. Robot. Autom., 1998, 14, (6), pp. 926939 (doi: 10.1109/70.736776).
    20. 20)
      • 4. Bajo, J., Tapia, D., Rodrguez, S., Luis, A., Corchado, J.: Nature-inspired planner agent for health care. 2007.
    21. 21)
      • 1. Ferber, J.: ‘Multi-agent system: an introduction to distributed artificial intelligence’ (Harlow: Addison Wesley Longman ISBN 0–201-36048-9, 1999).
    22. 22)
      • 15. Contet, J.M., Gechter, F., Gruer, P., Koukam, A.: ‘Bending virtual spring-damper: a solution to improve local platoon control’. Paper from the Int. Conf. Computational Science 2009 (ICCS 2009), (LNCS5544), Baton Rouge, Louisiana, USA, 25–27 May 2009.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2011.0125
Loading

Related content

content/journals/10.1049/iet-its.2011.0125
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading