Robot Planning with Ant Colony Optimization Algorithms

Abstract

Ant colony optimization algorithms are investigated in this paper for robot planning in configuration space. The robot planning problem is to find a feasible path from a beginning to a goal while avoiding obstacles in a clustered environment. Lots of attentions have been paid on such problems, but little is with the ant colony optimization algorithms. Originated from the max-min ant system (MMAS) algorithm for traveling salesman problem, a modified ant colony optimization algorithm for robot planning is proposed. The algorithm has some distinguished features, such as a path pruning mechanism, etc. The optimal solution can be achieved effectively in different environments with a high probability.