Time-varying environment-based machine learning technique for autonomous agent shortest-path planning

Dalila B. Megherbi; A. Teirelbar; A. J. Boulenouar

doi:10.1117/12.440003

20 September 2001 Time-varying environment-based machine learning technique for autonomous agent shortest-path planning

Dalila B. Megherbi, A. Teirelbar, A. J. Boulenouar

Proceedings Volume 4364, Unmanned Ground Vehicle Technology III; (2001) https://doi.org/10.1117/12.440003
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

Autonomous agent path planning is a main problem in the fields of machine learning and artificial intelligence. Reactive execution is often used in order to provide best decision for the agent's reactions. Although this problem is important in the stationary environment, most interesting environments are time varying. This paper is based on our previous work focusing on combining the potential field model with reinforcement learning to solve the stationary path problem. In this work we deal with the case of dynamic environment. In the dynamic environment, the motion of the obstacles provides for different problems and challenges, which our proposed algorithm in this paper encounters and addresses.

Citation Download Citation

Dalila B. Megherbi, A. Teirelbar, and A. J. Boulenouar "Time-varying environment-based machine learning technique for autonomous agent shortest-path planning", Proc. SPIE 4364, Unmanned Ground Vehicle Technology III, (20 September 2001); https://doi.org/10.1117/12.440003

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