Planning Maximum-Manipulability Cutting Paths

Abstract

This letter presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point cloud. We consider the multi-objective optimisation problem of finding robot trajectories which maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g., demolition of legacy nuclear plant, where the cut path needs not be precise as long as it achieves dismantling. We show how detours in the path can be leveraged to increase the manipulability of the robot at all points along the path. This helps to avoid singularities, while maximising the robot's capability to make small deviations during task execution. We show how a sampling-based planner can be projected onto the Riemannian manifold of a curved surface, and extended to include a term which maximises manipulability. We present the results of empirical experiments, with both simulated and real robots, which are tasked with moving over a variety of different surface shapes. Our planner enables successful task completion, while ensuring significantly greater manipulability when compared against a conventional RRT* planner.