A Predictive Path-Following Controller for Continuous Replanning With Dynamic Roadmaps

Abstract

This letter proposes a predictive path-following controller for efficient time parameterization during continuous replanning with dynamic roadmaps (DRMs). DRMs are an approach for fast global path planning in changing environments and are based on a precomputed graph in the configuration space and mappings from workspace voxels to vertices and edges in the graph. However, DRMs are a kinematic path planner and require subsequent steps of smoothing and trajectory generation before execution. This makes it hard to replan while the robot is moving, it is so because the time parameterization should account for the current position and velocity of the robot. To cope with this issue, a predictive path-following controller is presented, which computes the optimal control actions for moving the robot along the planned paths while respecting the bounds on maximum velocities and accelerations. Furthermore, it enables continuous replanning, i.e., the computation of new paths at fixed intervals, which is particularly useful in combination with a prediction of moving obstacles. The real-time capable approach is evaluated in simulation scenarios with moving obstacles and is experimentally validated for a dual-arm robot with 12 degrees of freedom.