Dynamic Path Planning with Stable Growing Neural Gas


This paper considers the problem of path planning under dynamic aspects. We propose Neural Gas Dynamic Path Planning (NGDPP), a novel algorithm that continuously provides a valid path between two points inside an environment that transforms in an unpredictable manner. These transformations can occur due to both, changes in the environment’s shape and moving collision objects. The algorithm incorporates several techniques: Neural Gas, a dynamic discretization method; the A* Algorithm, a path planning algorithm for graphs; and the Potential Field method, which facilitates the avoidance of collisions. We empirically evaluate the proposed algorithm under various aspects providing performance information and guidance about situations and applications benefiting from the algorithm. The evaluation reveals that NGDPP is a solid algorithm for path planning in dynamic environments. Yet, the algorithm is based on heuristic information, i.e. a optimal result in term of the path length cannot be guaranteed.

11th International Conference on Agents and Artificial Intelligence (ICAART 2019)