Autonomous Electric Vehicle Battery Disassembly Based on NeuroSymbolic Computing

TL;DR

This paper introduces a NeuroSymbolic framework enabling autonomous robots to efficiently and safely disassemble electric vehicle batteries in unstructured environments, improving recycling processes and reducing human risk.

Contribution

It presents a novel NeuroSymbolic task and motion planning approach for autonomous battery disassembly in complex environments, advancing robotic recycling technology.

Findings

Robots successfully disassemble batteries in complex environments

The framework handles obstacles and unstructured settings

Feasibility demonstrated through robotic experiments

Abstract

The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly. Due to the unstructured environment and high uncertainties, battery disassembly is still primarily done by humans, probably assisted by robots. It is highly desirable to design autonomous solutions to improve work efficiency and lower human risks in high voltage and toxic environments. This paper proposes a novel framework of the NeuroSymbolic task and motion planning method to disassemble batteries in an unstructured environment using robots automatically. It enables robots to independently locate and disassemble battery bolts, with or without obstacles. 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 disassembly tasks in a complex and dynamic environment.