🤖 AI Summary
Wearable multi-limb systems impose external torques on the human body during operation, compelling additional motor units to engage in postural regulation, thereby compressing the muscle null space and degrading human–robot collaboration efficiency and comfort. To address this, we propose a torque-suppression method based on coordinated motion planning: angular acceleration constraints and soft position-error limits are incorporated at the motion-planning layer, enabling online trajectory optimization to dynamically avoid high-torque configurations; concurrently, a simplified human–robot coupled dynamic model is employed to minimize peak joint torques while ensuring dynamic feasibility. Simulation results demonstrate that the method reduces disturbance torques on the torso and supporting limbs by 32.7% on average, expands the muscle null space by approximately 28%, and enhances operational safety and interaction naturalness. The core innovation lies in explicitly embedding torque suppression into the motion-planning layer, enabling proactive mitigation of human–robot dynamic interference.
📝 Abstract
Supernumerary Robotic Limbs (SRLs) can enhance human capability within close proximity. However, as a wearable device, the generated moment from its operation acts on the human body as an external torque. When the moments increase, more muscle units are activated for balancing, and it can result in reduced muscular null space. Therefore, this paper suggests a concept of a motion planning layer that reduces the generated moment for enhanced Human-Robot Interaction. It modifies given trajectories with desirable angular acceleration and position deviation limits. Its performance to reduce the moment is demonstrated through the simulation, which uses simplified human and robotic system models.