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A Novel Knowledge-induced Path Planning Strategy for The Mobile Robots


Guo Yi-nan, Yang Mei


Vol. 8  No. 5  pp. 30-35


Robot path planning is to obtain a most reasonable collision-free path in a certain environment. Existing method can ensure that the solution is the optimal or near-optimal path satisfying some criterion. However, the convergence speed and computation complexity of these methods are limited because they have not utilize knowledge embodied in the problem enough. Therefore, a novel knowledge-induced path planning strategy (KIPP) is proposed. Here, two kinds of knowledge, including explicit knowledge and implicit knowledge, are defined. Explicit knowledge memorizes the information about obstacles which is known in advance. The angle relationship between the path and the obstacles are extracted as implicit knowledge and used to judge and repair the infeasible path. Because the inserted point of repair operator is chosen from feasible region noted in implicit knowledge, the repaired path must be feasible after only repaired once. So computation complexity of this strategy is lower. Taken environment with regular or irregular obstacles as the example, simulation results show that the convergence speed and the precision of the solutions in the proposed strategy are better than other strategies.


Knowledge, Repair operator, Evolutionary algorithm, Path planning