Real-time collision avoidance with robot distance fields in a task-priority framework
Andrea Govoni, Michela Cavuoto, Yiming Li, Sylvain Calinon, Gianluca Palli · Robotics and Autonomous Systems · 2026
Safe and efficient collision avoidance is essential for robots operating in dynamic and cluttered environments. We present a task-priority control framework that embeds signed distance fields (SDFs) directly into the control loop, enabling smooth and reactive avoidance of both environmental and self-collisions. Robot links are represented with Bernstein polynomial-based distance fields, which provide continuous geometry models and closed-form gradients for defining repulsive actions. These avoidance behaviors are activated seamlessly within the task hierarchy and executed in real time through a GPU-accelerated implementation. The framework is validated on fixed-base and mobile manipulators exposed to dynamic obstacles sensed with depth cameras and laser scanners. Results show consistent improvements in responsiveness, computational efficiency and motion smoothness compared to conventional optimization-based approaches, demonstrating the effectiveness of integrating SDFs into task-priority control for robust robot motion in unstructured environments.