Adaptive Shared Control for Robot Manipulator Obstacle Avoidance with Dynamic Authority Allocation

Shared control improves robotic obstacle avoidance by combining human guidance and autonomous intervention, but existing methods struggle to balance them adaptively. To address this, we propose a shared control framework that dynamically adjusts control authority based on real-time obstacle proximity. The framework combines Weighted Quadratic Programming (WQP) with a sigmoid-based adaptive weighting mechanism for smooth transitions between operator-guided and autonomous collision-free trajectories. Autonomous trajectories are generated by a neural network policy trained on collision-free demonstrations from an enhanced A* algorithm. A comprehensive metric, Obstacle Avoidance Efficiency (OAE), is proposed to evaluate trajectory safety, efficiency, and risk exposure. Experiments validate the framework in two scenarios: static obstacle and suddenly placed obstacle. The experimental results demonstrate that our method outperforms direct operation, manual switching, and fixed-weight methods. It achieves higher success rates and better obstacle avoidance performance, effectively balancing human intent and safety.