Autonomous Navigation Control of Tracked Unmanned Vehicle Formation in Communication Restricted Environment

It is an important and difficult problem to control the cooperative operation of the ground unmanned system formation in the communication restricted environment of land battlefield. In this paper, the problem of autonomous navigation control of tracked unmanned vehicle formation in communication restricted environment is studied, and the Visual Limiting-Multi-agent Deep Deterministic Policy Gradient (VL-MADDPG) algorithm is proposed. First, in order to reduce the influence of large policy space and non-stationary environment on algorithm convergence, a visual limiting mechanism is proposed. By focusing the dependent information of the algorithm decision within the visual boundary, the communication requirement is further reduced while the state space is compressed, and the learning efficiency of the algorithm is improved. Then, a non-sparse reward function with time series is constructed based on the tracked vehicle motion model, and the policy is further optimized on the basis of synthesizing continuous action information. Finally, the VL-MADDPG algorithm is proposed and embedded into the autonomous navigation control task of tracked unmanned vehicle formation, which is the first attempt of collaborative control of tracked vehicles by related algorithms. The experimental results show that the proposed algorithm has good convergence and scalability compared with other excellent algorithms, and can complete the autonomous navigation control task of the tracked unmanned vehicle formation in the communication restricted environment.