Human-robot collaborative disassembly task planning for retired power battery based on Stackelberg game and multi-agent deep reinforcement learning

Abstract

With the increasing adoption of electric vehicles (EVs), the volume of retired power batteries has surged accordingly. In the context of sustainable development and a circular economy, the disassembly process of retired power battery for reuse is regarded as a crucial approach to addressing resource shortages and environmental pollution. To achieve the efficient disassembly of retired power batteries in human-robot collaborative (HRC) scenarios. Firstly, a mapping network between task units and task executors is established using a multilayer perceptron (MLP) neural network, based on the complexity of the tasks and the state of workers. Secondly, a Stackelberg Double Deep Q-Network (SDDQN) algorithm is proposed by integrating the leader-follower characteristics of the Stackelberg model with the Deep Q-Network (DQN) algorithm to address the task planning problem in HRC disassembly. Finally, the effectiveness of the proposed method is validated through two case studies. Compared to Nash Q-learning, Independent Q-learning, and the conventional DDQN algorithm, it demonstrates superior performance in terms of task completion time and average cumulative rewards. Additionally, the proposed method exhibits strong robustness against unexpected environmental disturbances.