Motion optimization for safe robot–environment interaction with force constraints

Abstract

Autonomous robotics working in the uncertain environment have drawn increasing interests from researchers. Here, an issue of online motion optimization under unknown environment is considered while preserving the safety and improving the flexible manoeuvrability of robot–environment interaction. This problem is addressed by improving the conventional dynamic movement primitives (DMPs) framework with force tracking constraints. First, an initial motion is learned through the DMPs. At the stage of skill generalization, a temporal coupling term combining with force constraints scheme which is inspired by the barrier Lyapunov function and finite‐time prescribed performance is deduced and adds to the original DMPs, so as to remain the contacting force staying within a predefined limit while aligning the motion along with surface of unknown environment adaptively. In this way, not only the contacting force can be guaranteed within a safe margin, but the shape of generalizing motion is preserved. Then the convergence and stability of the proposed DMPs are proved which is grounded on Laplace transformation‐based stability analysis to ensure the performance and safety. Finally, the proposed method is instantiated combined with conventional PID controller through the compared simulations to verify its effectiveness.