Joint spanning tree method improves robot arm surface coverage planning
Coverage Path Planning for Redundant Manipulators using Generalized Spanning Trees
Robotics
Summary
Covering a surface evenly with a robot arm that has many ways to move is tricky because there are many ways to position the arm at each spot. The authors created new planning methods that pick the best arm position for each part of the surface to make the arm move smoothly without repeating areas. One method plans everything ahead of time to save computing time and reduce how much the arm has to move. Another method plans step-by-step on the fly, which helps when the surface or environment changes suddenly.
What this means in practice
- •For industrial robot programmers: Generate efficient robot arm movements that cover complex surfaces without revisits, improving manufacturing tasks involving paint, inspection, or cleaning.
- •For drone operators in agriculture: Plan adaptive coverage paths for drones with robotic arms to treat crops even when field conditions change dynamically.
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.