Research on robot path planning based on reinforced ant colony optimization

DOI:	10.11809/bqzbgcxb2023.06.033
Keywords:	path planning; reinforcement learning; ant colony optimization (ACO); parameter optimization; artificial potential field algorithm
Abstract:	Aiming at the problems of ant colony optimization (ACO) in the process of optimal path searching, such as strong randomness in parameter selection, dependence on experience values, slow convergence speed and different parameter combinations affecting the convergence speed, this paper proposes an improved ACO based on reinforcement learning (RL) and artificial potential field (APF). Firstly, RL is used to configure the parameters of the ACO intelligently, which is called the RL ACO method. Secondly, based on the RL ACO, the local optimization mechanism of APF is introduced to carry out local path re planning for raster maps of different dimensions. During these processes, the RL ACO can solve the problem of complexity and randomness of the parameter selection process, as well as the dependence on empirical values through the intelligent parameter configuration in the specific environment, and improve the convergence speed of the algorithm. In addition, the introduction of the APF algorithm promotes the obstacle avoidance ability of the algorithm by reducing the number of inflection points of the local path, and achieves faster and smoother path planning effect. The simulation results show that, in an obstacle environment of different dimensions, the improved method can search the optimal path at a faster convergence speed and with fewer iterations. For the path planning in a complex high dimensional obstacle environment, the improved ACO algorithm demonstrates more obvious advantages in the convergence speed, iterations, obstacle avoidance ability and path smoothness. The ideas proposed in this paper are expected to be further extended and generalized to practical path planning, which has important practical significance and engineering values.
Published:	2023-06-28
PDF HTML