NeuroSymbolic Task and Motion Planner for Disassembly Electric Vehicle Batteries

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.