A full-coverage task assignment method for dynamic noise environments

Abstract

The invention discloses a full coverage task assignment method oriented to dynamic noise environment. First build a multi-robot environment composed of robot nodes and central control nodes; the robot nodes are equipped with a perception information acquisition module, an input state estimation module, a position relationship determination module, an interaction weight estimation module and an action estimation module; the central control node is equipped with an environment The state monitoring module, the experience acquisition module and the network update module; the perception information acquisition module obtains the partial view, the input state estimation module obtains the input state estimation vector and the numbered one-hot encoding vector, and the position relationship determination module calculates the adjacency feature matrix set, the interaction weight The estimation module calculates the adjacency weighted vector, and the action estimation module selects the maximum estimated action as a decision; the central control node adopts the reinforcement learning method to optimize the network in each module; the present invention performs optimization while performing, not only the task assignment is accurate and efficient, but also under the robot. The task execution time is shorter.

Description

technical field [0001] The invention relates to the field of intelligent robot systems and multi-agent reinforcement learning technology, in particular to a multi-robot full-coverage task assignment method based on multi-agent reinforcement learning, which can be used in a dynamic environment with noise. Background technique [0002] Due to its mobility, mobile robots can replace humans to perform tasks such as exploration, detection and operation in various complex or dangerous environments. After years of research and development, mobile robots have gradually become practical. In traditional fields such as manufacturing, logistics, and service industries, there have been many examples of using mobile robots to improve production efficiency or replace manual operations. In some projects of great strategic significance to the national economy, society, national defense and other fields, the demand for mobile robots is also increasingly obvious. With the continuous expansion...

AI Technical Summary

Problems solved by technology

2) When performing full-coverage task allocation tasks, there are often a lot of noise interference in the environment. For example, harsh environmental conditions may cause errors in the environmental perception information of a single robot, or hackers may control some robots to make their history Track record information is wrong

This requires the robot to learn to pay attention to the messages sent by the robots whose historical trajectories are around the robot's current position although the current distance is relatively far away. This requires an attention mechanism that accurately measures the interaction weight between each pair of robots. Most of the multi-agent reinforcement learning algorithms can only realize short-distance message attention weight calculation, and cannot balance the historical trajectory notification problem and the local target area conflict problem.

For example, if robot 1 and robot 3 are relatively close to each other, there is a potential conflict of goals and they should interact closely; similarly, robot 1 is about to explore the explored area of ​​robot 0, and the historical trajectory of robot 0 needs to be passed through message transmission. The form can tell robot 1 that although the two are far away at this time, the two should also interact closely to avoid repeated exploration, and the existing multi-agent reinforcement learning algorithm is difficult to balance the weight of these two parts of information

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