TOACA-DWA: A path planning algorithm for mobile robot in dynamic environments

Abstract

A dynamic path planning algorithm, named TOACA-DWA, is proposed by fusing the TOACA (two-layer optimal ant colony algorithm) and DWA (improved dynamic window approach). Firstly, in the TOACA algorithm, the pseudo-random state transfer rules are used for path selection, and the state transfer function is optimized to enhance search efficiency and global search capability. Secondly, a reward-punishment mechanism is proposed to update the pheromone, and we also limit the maximum and minimum bounds of pheromone concentration. These two measures can accelerate the algorithm’s convergence speed and prevent the algorithm from falling into the local optimum. By introducing a dynamic penalty factor, we reduce the pheromone concentration on the deadlock position. Finally, the algorithm introduces a crucial node screening strategy to delete the redundant nodes on the global path and use the improved DWA for local path planning between every two adjacent critical nodes of the global path. Simulation in dynamic environment is conducted to verify the real-time obstacle avoidance ability of the proposed algorithm. Furthermore, the real environment experiments further demonstrate the effectiveness of the proposed algorithm in practical applications.