Usv formation path-following method based on deep reinforcement learning

Abstract

The invention discloses an unmanned surface vessel (USV) formation path-following method based on deep reinforcement learning, which includes USV navigation environment exploration, reward function design, formation pattern keeping, a random braking mechanism and path following, wherein the USV navigation environment exploration is realized adopting simultaneous exploration by multiple underactuated USVs to extract environmental information, the reward function design includes the design of a formation pattern composition and a path following error, the path following controls USVs to move along a preset path by a leader-follower formation control strategy, and path following of all USVs in a formation is realized by constantly updating positions of the USVs. The invention accelerates the training of a USV path point following model through a collaborative exploration strategy, and combines the collaborative exploration strategy with the leader-follower formation control strategy to form the USV formation path following algorithm.