3D path planning for a robot based on improved ant colony algorithm

Abstract

Path planning is an important issue in the field of robotics research. Compared with traditional two-dimensional (2D) path planning, three-dimensional (3D) path planning is closer to practical applications. In this paper, a new improved ant colony algorithm is proposed to solve the problem of slow convergence speed, low efficiency and the tendency of falling into the local optimal solution of the traditional ant colony algorithm for the 3D path planning. There are three main improved steps in the novel ant colony algorithm in 3D path planning. In the first step, a pseudo-random state transition strategy is adopted to ensure the global search ability of the algorithm. In the second step, the pheromone update and the pheromone increment calculation method are used to accelerate the convergence speed of the algorithm which can ensure the quality of the solution. In the end step, a security value function of the heuristic function is used to ensure the security of path. In addition, the conditional fallback method is used to ensure the global search ability of the algorithm. Simulation results show that the new improved ant colony algorithm can find a feasible three-dimensional path quickly and efficiently.