Optimal Motion Planning Under Dynamic Risk Region for Safe Human–Robot Cooperation

As the development of factory automation, the workers and the robots are inevitable to collaborate in close proximity in a shared workspace, which makes the assurance of human safety a top priority. This article proposes a novel optimal motion planning framework for the manipulator to realize safe human–robot cooperation. To deal with the difficulties induced by the uncertainty and the sudden change of human movement, we design a novel dynamic risk region whose size is adjusted according to the predicted human velocity. Considering that the direct prediction of human velocity with low uncertainty is difficult due to the sensor noises and the errors involved with differential calculus, we first predict the human position at the next time step via Gaussian process regression, and then use it to predict the human velocity with the consideration of position prediction confidence. Then, we design the task controller by optimizing the performance index over an infinite time horizon, and design the safety-critical controller by extending the existing control barrier function-based method. Different from the existing works, we introduce a repulsive part to push the robot out when it enters the risk region, and provide an effective control gain design way to improve the adaptability in the dynamic environment. Finally, the simulation and experimental studies show that compared with the approaches with the fixed risk region and the simple proportional controller, our framework can get better trajectory tracking and safety performance in the dynamic environment.