This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.
The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.
By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.
This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.
