Benefiting from rapid growth in new energy vehicle industry,China’s population of new energy vehicles soared in rapid succession,percentage of power lithium battery rose from11%to 28%,in the future this trend will co...Benefiting from rapid growth in new energy vehicle industry,China’s population of new energy vehicles soared in rapid succession,percentage of power lithium battery rose from11%to 28%,in the future this trend will continue,it is expected that by 2020,the percentage of power lithium battery will reach47%.展开更多
After several years of development,the power battery recycling period has come.Calculated based on the number of power lithium batteries used by commercial vehicles(3-year battery life)and passenger vehicles(5-year ba...After several years of development,the power battery recycling period has come.Calculated based on the number of power lithium batteries used by commercial vehicles(3-year battery life)and passenger vehicles(5-year battery life),the capacity of scrapped power batteries in China is expected to reach 11.8Gwh by2018.展开更多
In current small batch and customized production mode,the products change rapidly and the personal demand increases sharply.Human-robot cooperation combining the advantages of human and robot is an effective way to so...In current small batch and customized production mode,the products change rapidly and the personal demand increases sharply.Human-robot cooperation combining the advantages of human and robot is an effective way to solve the complex assembly.However,the poor reusability of historical assembly knowledge reduces the adaptability of assembly system to different tasks.For cross-domain strategy transfer,we propose a human-robot cooperative assembly(HRCA)framework which consists of three main modules:expression of HRCA strategy,transferring of HRCA strategy,and adaptive planning of motion path.Based on the analysis of subject capability and component properties,the HRCA strategy suitable for specific tasks is designed.Then the reinforcement learning is established to optimize the parameters of target encoder for feature extraction.After classification and segmentation,the actor-critic model is built to realize the adaptive path planning with progressive neural network.Finally,the proposed framework is verified to adapt to the multi-variety environment,for example,power lithium batteries.展开更多
文摘Benefiting from rapid growth in new energy vehicle industry,China’s population of new energy vehicles soared in rapid succession,percentage of power lithium battery rose from11%to 28%,in the future this trend will continue,it is expected that by 2020,the percentage of power lithium battery will reach47%.
文摘After several years of development,the power battery recycling period has come.Calculated based on the number of power lithium batteries used by commercial vehicles(3-year battery life)and passenger vehicles(5-year battery life),the capacity of scrapped power batteries in China is expected to reach 11.8Gwh by2018.
基金the National Key Research and Development Program of China(No.2019YFB1706300)the National Natural Science Foundation of China(No.52075094)。
文摘In current small batch and customized production mode,the products change rapidly and the personal demand increases sharply.Human-robot cooperation combining the advantages of human and robot is an effective way to solve the complex assembly.However,the poor reusability of historical assembly knowledge reduces the adaptability of assembly system to different tasks.For cross-domain strategy transfer,we propose a human-robot cooperative assembly(HRCA)framework which consists of three main modules:expression of HRCA strategy,transferring of HRCA strategy,and adaptive planning of motion path.Based on the analysis of subject capability and component properties,the HRCA strategy suitable for specific tasks is designed.Then the reinforcement learning is established to optimize the parameters of target encoder for feature extraction.After classification and segmentation,the actor-critic model is built to realize the adaptive path planning with progressive neural network.Finally,the proposed framework is verified to adapt to the multi-variety environment,for example,power lithium batteries.
