Via‐Points Task Execution Based on Enhanced Dynamic Movement Primitives and Steering Force Fields

This paper explores trajectory planning for robotic arms in 3D operational spaces. To improve the adaptation to dynamic via‐points in complex, confined environments, an enhanced dynamic movement primitives (DMP) approach is proposed and designed for dynamic planning under composite steering force field constraints. By incorporating steering attraction forces, this method enhances the generalization capability of DMP, allowing the robotic arm to navigate through dynamic via‐points flexibly without altering the start and end positions. The trajectory shape is adjusted via regression attraction forces, which helps preserve the demonstrated trajectory, reduce free‐space loss, and improve the system's adaptability to complex, dynamic environments. The convergence of the target state is rigorously proven using Lyapunov stability theory. Numerical simulations and experiments conducted with the Franka robotic arm validate the effectiveness of the proposed approach. Results show that in dynamic environments with multiple via‐points, this method produces reliable trajectories for robotic arm movements, significantly enhancing the adaptation of DMP to dynamic contexts. The planning process requires no additional learning, and the generated trajectory closely resembles the original demonstrated path. This method enables effective via‐point operations in confined spaces without requiring additional learning, while maintaining existing skills.