Robot planning with artificial potential field guided ant colony optimization algorithm

  • Authors:

  • Dongbin Zhao;Jianqiang Yi

  • Affiliations:

  • Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
  • Year:
  • 2006

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Abstract

This paper investigates the problem of robot planning with ant colony optimization and artificial potential filed algorithms. Robot planning is to find a feasible path from a source to a goal while avoiding obstacles in configuration space. Artificial potential field (APF) is verified as an efficient method to find a path by following the maximum potential field gradient. But it suffers from the local minima. However, ant colony optimization (ACO) is characterized as powerful probabilistic search ability, which is thought to be fit for solving such local minima problems. By the combination of both merits, an APF guided ACO algorithm is proposed, which shows some good features in searching for the optimal path solution. The length optimal path solution can always be achieved with the proposed hybrid algorithm in different obstacles environment from simulation results.