A novel knowledge-driven flexible human-robot hybrid disassembly line and its key technologies for electric vehicle batteries

Based on the unique problems and challenges in the disassembly scenario of waste electric vehicle batteries (EVBs), we propose a knowledge-driven flexible human-robot hybrid disassembly line. The disassembly line can not only split the EVB disassembling tasks layer by layer to the primitive-level subtasks based on knowledge but also intelligently generate the optimal disassembling primitive sequence to complete the disassembling task. To further elaborate on the key technologies of the above-mentioned disassembly line, we focus on the implementation of the high-accuracy screw disassembly and disassembly planning system based on NeuroSymbolic. Implementation of the high-accuracy screw disassembly is realized by a new design of the disassembly actuator, high-accuracy screw position and pose estimation based on visual perception, and robust insertion based on force perception. The disassembly planning system is constructed by defining disassembly primitives and introducing neural predicates which can take the sensor data as input and output the symbolic states. Experiments show that our system can effectively solve the screw disassembling task in complex disassembly scenarios.