Multi-mode filter target tracking method for mobile robot using multi-agent reinforcement learning

Abstract

Multi-mode filtering target tracking for mobile robot has important research significance for robot path planning, motion control and tracking robot targets. To address the problem that it is difficult for mobile robot to track targets in unknown environments, a multi-mode filtering target tracking method for mobile robot using multi-agent reinforcement learning is proposed. First, based on the extended kalman filter and combined with probability data association, the observation values in different motion modes are calculated and the system state is updated in real time, to achieve concurrent mapping, location and state estimation for each robot. Second, state communication among multi-mobile robot are established, and the multi-agent reinforcement learning model is built. Finally, based on the agent reinforcement learning model, based on the communication and communication among multi-tracking agents and the location of the target agent, the target tracking task is allocated to shorten the total path of the tracking agent, and the multi-target tracking of multi-mobile robot is completed by constantly learning and updating the strategy. The results show that the maximum tracking error of the target is 7 mm, and the calculation time of each step is only 51.1ms. The tracking success rate, observation rate and coverage rate are all high. It has important research value in robot location and navigation, service robot and other fields.