Multi-inspection-robot collaborative path planning method and system based on DQN model
The invention discloses a multi-inspection-robot cooperative path planning method and system based on a DQN model. The method comprises the steps that position coordinates of all inspection robots and arrival states of all task points are obtained, and a multi-inspection-robot cooperative state spac...
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Main Authors | , , , , , , , |
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Format | Patent |
Language | Chinese English |
Published |
04.07.2023
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Abstract | The invention discloses a multi-inspection-robot cooperative path planning method and system based on a DQN model. The method comprises the steps that position coordinates of all inspection robots and arrival states of all task points are obtained, and a multi-inspection-robot cooperative state space is constructed; acquiring moving directions selected by all the inspection robots to construct a cooperative action space of the multiple inspection robots; classifying the states by taking an anti-collision mechanism triggered between the inspection robots and the obstacles and an anti-collision mechanism triggered between the inspection robots as constraint conditions, and defining reward values corresponding to various states; according to the state, the action and the reward value, the DQN model calculates the expectation of a return value obtained after the set action is executed in the set state, the expectation is optimized through deep neural network parameter training, and the state and the action corres |
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AbstractList | The invention discloses a multi-inspection-robot cooperative path planning method and system based on a DQN model. The method comprises the steps that position coordinates of all inspection robots and arrival states of all task points are obtained, and a multi-inspection-robot cooperative state space is constructed; acquiring moving directions selected by all the inspection robots to construct a cooperative action space of the multiple inspection robots; classifying the states by taking an anti-collision mechanism triggered between the inspection robots and the obstacles and an anti-collision mechanism triggered between the inspection robots as constraint conditions, and defining reward values corresponding to various states; according to the state, the action and the reward value, the DQN model calculates the expectation of a return value obtained after the set action is executed in the set state, the expectation is optimized through deep neural network parameter training, and the state and the action corres |
Author | QI MANSHUN ZHANG RUI ZHANG HAIHUA CHEN HAO FANG GUOQUAN QIAN QILONG CAI BIAO HAN XIANGZHENG |
Author_xml | – fullname: ZHANG RUI – fullname: QI MANSHUN – fullname: CHEN HAO – fullname: ZHANG HAIHUA – fullname: FANG GUOQUAN – fullname: CAI BIAO – fullname: HAN XIANGZHENG – fullname: QIAN QILONG |
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DocumentTitleAlternate | 基于DQN模型的多巡检机器人协同路径规划方法及系统 |
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Snippet | The invention discloses a multi-inspection-robot cooperative path planning method and system based on a DQN model. The method comprises the steps that position... |
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Title | Multi-inspection-robot collaborative path planning method and system based on DQN model |
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