Year: 2025
Type: article
Abstract: With the rapid growth of electric vehicles, the efficient and safe recycling of high-energy battery packs, particularly the removal of structural bolts, has become a critical challenge. This study presents a NeuroSymbolic robotic system for battery disassembly, driven by autonomous learning capabilities. The system integrates deep perception modules, symbolic reasoning, and action primitives to achieve interpretable and efficient disassembly. To improve adaptability, we introduce an offline learning framework driven by a large language model (LLM), which analyzes historical disassembly trajectories and generates optimized action sequences via prompt-based reasoning. This enables the synthesis of new action primitives tailored to familiar scenarios. The system is validated on a real-world UR10e robotic platform across various battery configurations. Experimental results show a 17 s reduction in average disassembly time per bolt and a 154.4% improvement in overall efficiency compared with traditional approaches. These findings demonstrate that combining neural perception, symbolic reasoning, and LLM-guided learning significantly enhances robotic disassembly performance and offers strong potential for generalization in future battery recycling applications.
Source: Batteries
Authors: Pengxu Chang, Zhigang Wang, Yanlong Peng, Ziwen He, Ming Chen
Institutions: Shanghai Jiao Tong University, Intel (United States)
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
Award: G3374312384
OpenAlex ID: W4414017449