Multi-underwater-robot formation control method based on reinforcement learning

Abstract

The invention discloses a multi-underwater-robot formation control method based on reinforcement learning, and relates to the field of control of underwater robots. In the invention, after each robotnode in an underwater robot formation acquires the position of the robot node, a control center gives track information of a virtual leader and sends the track information to neighbor nodes of the virtual leader; a topological communication network is established among underwater robot nodes, and each underwater robot node only communicates with the neighbor nodes to keep the formation stable; theunderwater robot formation uses the current control strategy to track, each node calculates a one-step cost function by interacting with the environment and the neighboring nodes, the current controlstrategy is improved by minimizing a value function, when two steps including value iteration and strategy improvement both reach convergence, the control strategy of the underwater robots tracking an expected trajectory is optimal, and the optimal control strategy is used to achieve the goal of accurate tracking.

Description

technical field [0001] The invention relates to the field of underwater robot control, in particular to a multi-underwater robot formation control method based on reinforcement learning. Background technique [0002] With the increasingly widespread application of marine resources, the control technology of underwater robots has achieved unprecedented development. An important application of underwater robots in the ocean is trajectory tracking, but the tracking efficiency of a single underwater robot is often low, and the tracking error will increase cumulatively. For some special tasks, underwater robot formations are required to coordinate tasks, but the underwater environment is complex Changeable, underwater robot model parameters are difficult to obtain accurately, and robot formation control is difficult. [0003] In the prior art, the publication number is CN107748566A, titled: A method for controlling the fixed depth of an underwater autonomous robot based on reinf...

AI Technical Summary

Problems solved by technology

However, the underwater environment is complex and changeable, and the effects of surge, swing, and heave are likely to seriously affect the vision of underwater robots, making formation control impossible.

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