Search papers, labs, and topics across Lattice.
This paper addresses the challenge of surface coverage with task-redundant manipulators by extending the classical Spanning Tree Coverage (STC) method to develop Joint Spanning Tree Coverage (JSTC) algorithms. The offline JSTC algorithm utilizes a Generalized Minimum Spanning Tree (GMST) approach to efficiently select configurations for each grid cell, resulting in a non-revisiting coverage path that minimizes computation time and joint motion. In dynamic scenarios, the online JSTC algorithm enables rapid planning by incrementally expanding and backtracking the spanning tree, demonstrating significant improvements over existing methods in both computation efficiency and adaptability.
Offline JSTC slashes computation time and joint motion while ensuring non-revisiting coverage paths for redundant manipulators.
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.
