Hybrid chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots. (28th June 2019)

• Record Type: Journal Article
• Authors: Chen, Peng; Li, Qing; Zhang, Chao; Cui, Jiarui; Zhou, Hao
• Is Part Of: International journal of advanced robotic systems. Volume 16:Number 4(2019:Jul./Aug.)
• Publication Date: 2019-06-28
• Subjects: Landfill -- robots -- path planning -- PSO -- ACO -- chaos -- pheromone -- iterations; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881419859083
• Languages: English
• ISSNs: 1729-8806

Abstract

Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In these assignments, one important issue is to obtain appropriate paths for robots especially in some complex applications. In this context, a novel hybrid swarm intelligence algorithm, ant colony optimization enhanced by chaos-based particle swarm optimization, is proposed in this article to deal with the path planning problem for landfill inspection robots in Asahikawa, Japan. In chaos-based particle swarm optimization, Chebyshev chaotic sequence is used to generate the random factors for particle swarm optimization updating formula so as to effectively adjust particle swarm optimization parameters. This improved model is applied to optimize and determine the hyper parameters for ant colony optimization. In addition, an improved pheromone updating strategy which combines the global asynchronous feature and "Elitist Strategy" is employed in ant colony optimization in order to use global information more appropriately. Therefore, the iteration number of ant colony optimization invoked by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively. Comparative simulation experiments show that the chaos-based particle swarm optimization-ant colony optimization has a rapid search speed and can obtain solutions with similar qualities.