Towards human-centric manufacturing: A reinforcement learning method for physical exertion alleviation in HRCA

Abstract

The advancement of the manufacturing system towards more human-centric, emphasising not only efficiency but also the well-being of workers. However, task planning in human–robot collaborative assembly (HRCA) remains challenging, when considering the physical exertion alleviation of workers, due to the complexities of physical exertion estimation and variations in human assembly operations. Different from conventional methods, this paper proposes a task planning method for physical exertion alleviation of workers in HRCA by leveraging the reinforcement learning (RL) method to train a policy. Initially, a musculoskeletal model-based method driven by human movement data to assess workers’ physical exertion is integrated into this work. Then, the policy is trained in a DuelingDQN-AM framework, utilising a carefully designed reward function informed by the estimated physical exertion of workers. The effectiveness of this approach has been validated through a simulation experiment and a proof-of-concept real assembly experiment. Simulation experiment results demonstrate the advantages of DuelingDQN-AM over other methods in terms of convergence speed and stability across multiple cycles and products of varying complexity. Additionally, real-world experiment results show that the RL strategy reduced physical exertion by 15.63 % compared to the baseline method. • A reinforcement learning-based framework aimed at alleviating physical exertion in HRC was proposed. • An RL algorithm, DuelingDQN-AM, informed by muscle physical exertion, was introduced. • Numerical experiments and multi-subject real assembly experiments were conducted for method validation.