Abstract
A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.
Highlights
Mobile robots have received widespread attention because of their great potential and research value in industrial applications, manufacturing, search and rescue, medical service, and intelligent transportation system [1]
The global path planning method is applied to a static environment in the paper
The critical obstacle influence factor is proposed for the initial pheromone distribution
Summary
Mobile robots have received widespread attention because of their great potential and research value in industrial applications, manufacturing, search and rescue, medical service, and intelligent transportation system [1]. The path planning of mobile service robots generally considers both static and dynamic environments [4]. One is the robot plans route offline with the known map information, which is called global path planning [5]. In another type, the robot does not input environmental information in advance. It is necessary to use the sensor to establish an environmental map in real time, avoid obstacles, and find a suitable path. This kind of path planning is called local path planning [6]. The global path planning method is applied to a static environment in the paper
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