Motion planning in order to optimize the length and clearance applying a Hopfield neural network
This paper deals with motion planning in plane for a mobile robot with two freedom degrees through some polygonal unmoved obstacles. Applying Minkowski sum, we can represent the robot as a point. Then, by using traditional approaches such as visibility graphs, simple and generalized Voronoi diagrams, decomposition methods, etc, it is possible to provide a graph covering obstacles, say roadmap. In order to find a real-time collision-free robot motion planning between two arbitrary source and target configurations through the roadmap, an adoptive Hopfield neural network is considered. Maximizing the clearance of path together with minimizing the length of path are pursued in a bi-objective framework. For treating with multiple objectives TOPSIS method, as a kind of goal programming techniques, is provided to find the efficient solutions. Because of capability of parallel computation through hardware implementation of neural networks, the presented approach is a reasonable technique in mobile robot navigation and traveler guidance systems. The advantages of the proposed system are confirmed by simulation experiments. This approach can be directly extended in unknown environment including time-varying conditions.
neural network multi-objective online routing parallel implementation