Planning efficient robot arm paths for covering surfaces with many joint options
Coverage Path Planning for Redundant Manipulators using Generalized Spanning Trees
Robotics
Summary
Covering a surface with a robot arm that has many ways to bend its joints is hard because there are many possible arm positions for each spot on the surface. The authors made new computer methods that help the robot pick the best joint positions to cover the whole surface smoothly without going over the same spot twice. One method plans the whole path before starting, which saves time and motion. The other method plans step-by-step and can react quickly if the surface changes. Their tests show these ways cut down wasted movement and speed up planning compared to older methods.
coverage path planningredundant manipulatorsinverse kinematicsspanning tree coveragegeneralized minimum spanning treerobot motion planningoffline planningonline planningconfiguration space
Authors
Raksi Kopo, Kostas J. Kyriakopoulos
Abstract
Surface coverage with task-redundant manipulators is challenging because each surface point may admit multiple inverse kinematics (IK) solutions, and configuration choices strongly affect motion quality. This paper extends the classical Spanning Tree Coverage (STC) method to redundant manipulators through offline and online Joint Spanning Tree Coverage (JSTC) algorithms. Offline JSTC samples multiple Inverse Kinematics (IK) solutions per grid cell and formulates the problem as a Generalized Minimum Spanning Tree (GMST), selecting one configuration per cell and tracing the resulting tree to obtain a non-revisiting coverage path. Online JSTC incrementally expands and backtracks a spanning tree with feasibility and cost evaluation while handling dynamic grid updates. Simulation results show that offline JSTC reduces computation time, reconfigurations, and joint motion compared to other methods, while online JSTC achieves fast per-step planning in dynamic scenarios.