Evolving robotic path with genetically optimised fuzzy planner
by Rahul Kala, Ritu Tiwari, Anupam Shukla
International Journal of Computational Vision and Robotics (IJCVR), Vol. 1, No. 4, 2010

Abstract: Path planning is one of the highly studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper, we evolve the robotic path using genetic algorithms (GA). The GA generates solutions for the static map which disobeys the non-holonomic constraints. Fuzzy inference system (FIS) works on the generated path and extends the problem for dynamic environment. The results of GA serve as a guide for FIS planner. The FIS system was initially generated using rules from common sense. Once this model was ready, the fuzzy parameters were optimised by another GA. The GA tried to optimise the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to execute the plan of the robot in a dynamic environment. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. Hence, using a combination of GA along with FIS, we were able to solve the problem of robotic path planning.

Online publication date: Fri, 21-Jan-2011

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