Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72618
Title: Evolutionary Artificial Potential Fields and their application in real time robot path planning
Authors: Vadakkepat, Prahlad 
Tan, Kay Chen 
Ming-Liang, Wang
Issue Date: 2000
Source: Vadakkepat, Prahlad,Tan, Kay Chen,Ming-Liang, Wang (2000). Evolutionary Artificial Potential Fields and their application in real time robot path planning. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC 1 : 256-263. ScholarBank@NUS Repository.
Abstract: A new methodology named Evolutionary Artificial Potential Field (EAPF) is proposed for real-time robot path planning. The artificial potential field method is combined with genetic algorithms, to derive optimal potential field functions. The proposed Evolutionary Artificial Potential Field approach is capable of navigating robot(s) situated among moving obstacles. Potential field functions for obstacles and goal points are also defined. The potential field functions for obstacles contain tunable parameters. Multi-objective evolutionary algorithm (MOEA) is utilized to identify the optimal potential field functions. Fitness functions like, goal-factor, obstacle-factor, smoothness-factor and minimum-path-length-factor are developed for the MOEA selection criteria. An algorithm named escape-force is introduced to avoid the local minima associated with EAPF. Moving obstacles and moving goal positions were considered to test the robust performance of the proposed methodology. The simulation results showed that the proposed methodology is efficient and robust for robot path planning with non-stationary goals and obstacles.
Source Title: Proceedings of the IEEE Conference on Evolutionary Computation, ICEC
URI: http://scholarbank.nus.edu.sg/handle/10635/72618
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