Abstract
Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.
Highlights
With the rapid development of mobile robots, path planning has become the foundation and core of the research field of mobile robots. e path planning technology of mobile robot is to find an optimal or suboptimal collision-free path from the beginning to the end in a complex environment according to certain evaluation criteria, such as the shortest route, the least turning, the least energy consumption, etc. [1]. e traditional algorithms to solve the path planning problem mainly include artificial potential field algorithm [2], Dijkstra algorithm [3], and A∗ algorithm [4]
There are still problems such as slow convergence rate and easy to fall into local optimum in robot path planning
This paper proposes an improved ant colony algorithm based on two-dimensional path smoothing factor for mobile robot path planning
Summary
With the rapid development of mobile robots, path planning has become the foundation and core of the research field of mobile robots. e path planning technology of mobile robot is to find an optimal or suboptimal collision-free path from the beginning to the end in a complex environment according to certain evaluation criteria, such as the shortest route, the least turning, the least energy consumption, etc. [1]. e traditional algorithms to solve the path planning problem mainly include artificial potential field algorithm [2], Dijkstra algorithm [3], and A∗ algorithm [4]. It simulates the foraging process of the ant colony and obtains the solution path jointly planned by the ants. It has the characteristics of positive feedback, parallel computation, and easy fusion. Cao et al proposed to build an initial pheromone model to avoid blind search and improve the convergence speed of the traditional ant colony algorithm [14]. Zhang et al proposed to construct a new heuristic function to make the pheromone volatile factor adapt to change and to ensure rapid convergence of ants even when searching the path comprehensively [16]. This paper proposes an improved ant colony algorithm based on two-dimensional path smoothing factor for mobile robot path planning
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