ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (12): 2604-2617.doi: 10.7544/issn1000-1239.2021.20211002

所属专题: 2021可解释智能学习方法及其应用专题

• 人工智能 • 上一篇    下一篇



  1. 1(上海交通大学机械与动力工程学院 上海 200240);2(英特尔中国研究院 北京 100086) (
  • 出版日期: 2021-12-01
  • 基金资助: 

NeuroSymbolic Task and Motion Planner for Disassembly Electric Vehicle Batteries

Ren Wei1, Wang Zhigang2, Yang Hua2, Zhang Yisheng1, Chen Ming1   

  1. 1(School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240);2(Intel Labs China, Beijing 100086)
  • Online: 2021-12-01
  • Supported by: 
    This work was supported by 2021 High Quality Development Project of Ministry of Industry and Information Technology of China (TC210H02C).

摘要: 建立完善的动力电池回收利用体系是我国新能源汽车高质量发展需要突破的瓶颈问题之一,研究和发展智能化、柔性化、精细化的高效拆解技术是其中的重要环节.但由于受非结构化的拆解环境和拆解过程中的不确定性等因素的影响,目前,动力电池拆解还采用人工为主、机器辅助拆解的方式,不仅低效,而且致使工作人员暴露在危险的工作环境中,亟需向自动化、智能化方式转变.研究基于神经符号理论对动态环境中动力电池的拆解任务进行研究,设计并实现了一套任务和运动规划系统.与现有的动力电池拆解系统相比,系统在自主性、可扩展性、可解释性、可学习性4方面具备明显的优势,这4方面的优势相辅相成,可以不断促进系统的完善和提高,为实现动力电池的智能化拆解铺平了道路.基于该系统实现了在复杂多变的拆解工作环境中动力电池连接约束件的智能拆解,验证了系统的可行性.

关键词: 神经符号, 可解释AI, 机器人, 动力电池, 拆解, 任务和运动规划

Abstract: Establishing a perfect electric vehicle battery recycling system is one of the bottlenecks that need to be broken through in pursuit of high-quality development of new energy vehicles in our country. Disassembly technology will play an important role in research and development of intelligent, flexible, and refined high-efficiency. Due to its unstructured environment and high uncertainties, disassembling batteries is primarily accomplished by humans with a fixed robot-assisted battery disassembly workstation. This method is highly inefficient and in dire need of being upgraded to an automated and intelligent one to exempt humans from being exposed to the high voltage and toxic working conditions. The process of removing and sorting electric vehicle batteries represents a significant challenge to the automation industry since used batteries are of distinctive specifications that renders pre-programming impossible. A novel framework for NeuroSymbolic based task and motion planning method to automatically disassemble batteries in unstructured environment using robots is proposed. It enables robots to independently locate and loose battery bolts, with or without obstacles. This method has advantages in its autonomy, scalability, explicability, and learnability. These advantages pave the way for more accurate and robust system to disassemble electric vehicle battery packs using robots. This study not only provides a solution for intelligently disassembling electric vehicle batteries, but also verifies its feasibility through a set of test results with the robot accomplishing the disassemble task in a complex and dynamic environment.

Key words: NeuroSymbolic, explainable AI, robotic, electric vehicle battery, disassembly, task and motion planning