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Synthesis of reflexive algorithms with intelligence for effective robot path planning in unknown environments

The ability of a robot to find a goal in an unknown environment is significantly improved from previous path planners with the synthesis of intelligence and reflexive algorithms as a path planning approach. A hybrid expert system vector field (HESV) path planner is introduced and tested in 100 map scenarios using a custom robotic simulation shell. The method combines a trap detection expert system, a low-level reflexive obstacle avoidance algorithm, and a trap evasion expert system, to achieve improved performance without sacrificing computational efficiency. Simulation results and evaluations are presented for the potential field, vector field, and HESV techniques. HESV's expert systems have been simplified to illustrate the performance improvements that result from a small amount of intelligence. Although recommendations for the structure of the path planning system are given, the purpose of the paper is to show the effectiveness of intelligent rules in path planning, rather than to define the actual rules to be used.