Hybrid chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots

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.

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

“…Both Elmi and Topaloglu take into account the loading and unloading time of robots. 44,45 Chen et al 46,47 designed two algorithms to systematically schedule the multi-robot to obtain resources and objective to improve the cost efficiency.…”

Computer vision-based techniques and path planning strategy in a slope monitoring system using unmanned aerial vehicle

“…When the cost between every two nodes are determined, the path planning problem is transformed into a TSP which can be solved by many optimization algorithms. 46,47…”

Research on Multi-AGVs Path Planning and Coordination Mechanism

“…Du et al [17] use hybrid genetic particle swarm optimization algorithm (GA-PSO) to plan the path, and propose a particle iteration mechanism based on time priority, which makes the algorithm directional search path and accelerates the convergence speed of the algorithm. Chen et al [18] employ an improved pheromone updating strategy which combines the global asynchronous feature and "Elitist Strategy", this method emphasizes the influence of the best ant (the individual with the current shortest solution) by "Elitist Strategy". Therefore, the iteration number of ACO algorithm invokes by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively.…”

“…The concentration of pheromone remaining on the path determines the path the ant will take [26]. The more ants that travel on a certain path, the greater the pheromone concentration on the path [27] [18]. The following ants have a high probability to choose the path with high pheromone concentration, and the optimal path becomes clear [28].…”

“…This paper uses the grid method to establish the AGV working environment map, and set the task starting node (3,18) and the target node (17,3). The size of the map is 20 × 20 , the obstacles are randomly distributed with a probability of 30%, there are no obstacles at the location of the starting node and the location of the target node.…”