  The efficient disassembly of end-of-life electric vehicle batteries(EOL-EVBs)
is crucial for green manufacturing and sustainable development. The current
pre-programmed disassembly conducted by the Autonomous Mobile Manipulator
Robot(AMMR) struggles to meet the disassembly requirements in dynamic
environments, complex scenarios, and unstructured processes. In this paper, we
propose a Battery Disassembly AMMR(BEAM-1) system based on NeuralSymbolic AI.
It detects the environmental state by leveraging a combination of multi-sensors
and neural predicates and then translates this information into a
quasi-symbolic space. In real-time, it identifies the optimal sequence of
action primitives through LLM-heuristic tree search, ensuring high-precision
execution of these primitives. Additionally, it employs positional speculative
sampling using intuitive networks and achieves the disassembly of various bolt
types with a meticulously designed end-effector. Importantly, BEAM-1 is a
continuously learning embodied intelligence system capable of subjective
reasoning like a human, and possessing intuition. A large number of real scene
experiments have proved that it can autonomously perceive, decide, and execute
to complete the continuous disassembly of bolts in multiple, multi-category,
and complex situations, with a success rate of 98.78%. This research attempts
to use NeuroSymbolic AI to give robots real autonomous reasoning, planning, and
learning capabilities. BEAM-1 realizes the revolution of battery disassembly.
Its framework can be easily ported to any robotic system to realize different
application scenarios, which provides a ground-breaking idea for the design and
implementation of future embodied intelligent robotic systems.
