A Smooth Trajectory Planning for a Semi Autonomous Wheelchair Using Particle Swarm Optimization
Main Article Content
Abstract
This paper presented a motion planning algorithm for a semi autonomous wheelchair for severely disable people. The D* was used to determine an optimal path to its goal position. An optimal motion planning using particle swarm optimization (PSO) to navigate the wheelchair to its global goal. The D* search guarantees a global path from the start position to the goal if there is any while the particle swarm optimization search provides a smooth movement for the wheelchair from one step to another. The simulation in Player/Stage showed that the wheelchair could find an optimal path and the planned trajectory was smoother compared with the motion planner using potential field only.
Keywords
Semi-autonomous wheelchair, Particle swarm optimization, Motion planning
Article Details
References
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[2] Simpson RC, LoPresti EF, Hayashi S, Guo S. Ding D, Cooper RA., "Smart Power Assistance Module for manual wheelchairs. Technology and Disability: Research, Design, Practice and Policy", 26th International Annual Conference on Assistive Technology for People with Disabilities (RESNA)
[3] Parikh SP, Rao RS, Jung SH, Kumar V, Ostrowski JP, Taylor CJ., "Human robot interaction and usability studies for a smart wheelchair", Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2003 Oct 27-31; Las Vegas, NV. Piscataway (NJ), 2003. p. 3206-11.
[4] http://as.ms.t.kanazawa-u.ac.jp/as-e.html, accessed on May 14th 2016.
[5] G. E. Fabiani, D. J. McFarland, J. R. Wolpaw, and G. Pfurtscheller, "Conversion of EEG Activity into Cursor Movement by a Brain- computer Interface", Transactions on Neural Systems and Rehabilitation Engineering, vol. 12, pp. 331-338, 2004.
[6] D. J. Krusienski and J. J. Shih, "A Case Study on the Relation Between Electroencephalographic and Electrocorticographic Event-Related Potentials", in the 32nd Annual International Conference of the IEEE EMBS, 2010.
[7] D. Murray and J. J. Little, "Using Real-Time Stereo Vision for Mobile Robot Navigation". Autonomous Robots, vol. 8, pp. 161-171, 2000.
[8] D. J. McFarland and J. R. Wolpaw, "EEG-Based Communication and Control:Speed-Accuracy Relationships", Applied Psychophysiology and Biofeedback, vol. 28, pp. 217-231, 2003.
[9] Y. Yamamoto, and X. Yun, "Coordinating locomotion and manipulation of a mobile maniplator", in Recent trends in mobile robots, vol. Y.F. Zheng, Editor. 1993, World Scientfic: Singapor; London, pp. 157-181.
[10] S. Anthony, "Optimal and Efficient Path Planning for Partially-Known Environments", Proceedings of the International Conference on Robotics and Automation: 3310-3317, 1994
[11] J. Kennedy and R. Eberhart, "Particle swarm optimization", in Proc IEEE International Conference on Neural Networks, Dec. 1995, pp. 1942-1948.
[12] B. Gerkey, R. Vaughan, and A. Howard, "The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems", Proceedings of the International Conference on Advanced Robotics 317-323, 2003.
[13] C. Sacramento, "Potential field methods and their inherent limitations for mobile robot navigation".. Robotics and Automation, IEEE International Conference on August 2002: p. 345-402.