The disassembly of end-of-life lithium–ion batteries (EOL-LIBs) is inherently complex, owing to their multi-state and multi-type characteristics. To mitigate these challenges, a human–robot collaboration disassembly (HRCD) model is developed. This model capitalizes on the cognitive abilities of humans combined with the advanced automation capabilities of robots, thereby substantially improving the disassembly process’s flexibility and efficiency. Consequently, this method has become the benchmark for disassembling EOL-LIBs, given its enhanced ability to manage intricate and adaptable disassembly tasks. Furthermore, effective disassembly sequence planning (DSP) for components is crucial for guiding the entire disassembly process. Therefore, this research proposes an approach for the generation of HRCD sequences for EOL-LIBs based on knowledge graph, providing assistance to individuals lacking relevant knowledge to complete disassembly tasks. Firstly, a well-defined disassembly process knowledge graph integrates structural information from CAD models and disassembly operating procedure. Based on the acquired information, DSP is conducted to generate a disassembly sequence knowledge graph (DSKG), which serves as a repository in graphical form. Subsequently, knowledge graph matching is employed to align nodes in the existing DSKG, thereby reusing node sequence knowledge and completing the sequence information for the target disassembly task. Finally, the proposed method is validated using retired power LIBs as a case study product.

With the increasing demand for humans and robots to collaborate in a joint workspace, it is essential that robots react and adapt instantaneously to unforeseen events to ensure safety. Constraining robot dynamics directly on SE(3), that is, the group of 3D translation and rotation, is essential to comply with the emerging Human–Robot Collaboration (HRC) safety standard ISO/TS 15066. We argue that limiting coordinate-independent magnitudes of physical dynamic quantities at the same time allows more intuitive constraint definitions. We present the first real-time capable online trajectory generator that constrains translational and rotational magnitude values of 3D translation and 3D rotation dynamics in a singularity-free formulation. Simulations as well as experiments on a hardware platform show the utility in HRC contexts.

This paper presents a comprehensive analysis of time and action operational costs on an objective function developed by Bechar et al. (A. Bechar, J. Meyer and Y. Edan, “An objective function to evaluate performance of human–robot collaboration in target recognition tasks,” IEEE Trans. Syst. Man Cybern. Part C39(6), 611–620 (2009)) for collaborative target recognition systems. Different task types, system reaction types, and environments were evaluated. Results reveal two types of task and system reactions – one focused on minimizing false alarms, and the second on detecting a target when one is presented. In addition, the analysis reveals a new property of the objective function based on a specific ratio between the weight differences that generalizes the model's objective function and facilitates its analysis. Results indicate that human decision time strongly influences system performance.

This paper presents a novel control architecture for humanoid robot RH-2. The main objective is that a robot can perform different tasks in collaboration with humans in working environments. In order to achieve this goal, two control loops have to be defined. The outer loop, called collaborative control loop, is devoted to the generation of stable motion patterns for a robot, given a specific manipulation task. The inner loop, called posture stability control loop, acts to guarantee the stability of humanoid for different poses determined by motion patterns. A case study is presented in order to show the effectiveness of the proposed control architecture.