A novel backup path planning approach with ACO

Abstract

Today's robot path planning deals with finding a sufficiently good or optimal path to the destination by considering changes and avoiding obstacles in the environment. The gathered information is processed and — if necessary — alternative paths are generated. This paper proposes a novel Backup Path Planning Approach (BPPA) by means of reusing the distributed pheromones of the Ant Colony Optimization (ACO) approach. The proposed strategy and algorithm derive feasible backup paths solely from the available pheromone concentration. The results of the experiment reveal that BPPA explores new paths which are suitable to detour small clusters of newly appeared obstacles. However, the incorporation of these tours shrinks the original path and generates an additional benefit.