Asymptotically Optimized Multi-Surface Coverage Path Planning for Loco-Manipulation in Inspection and Monitoring

Recommended citation: Kim Tien Ly, Matthew Munks, Wolfgang Merkt, and Ioannis Havoutis. Asymptotically Optimized Multi-Surface Coverage Path Planning for Loco-Manipulation in Inspection and Monitoring. Proc. IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023.

Regular inspection and monitoring of aging assets are crucial to safe operation in industrial facilities, with remote robotic monitoring being a particularly promising approach for asset inspection. However, vessels, pipework, and surfaces to be monitored can follow complex 3D surfaces, and frequently no 3D as-built models exist. In this paper, we present an end-to-end solution that uses an optimization method for coverage path planning of multiple complex surfaces for mobile robot manipulators. The system includes a two-layer hierarchical structure of optimization: mission planning and motion planning. The surface sequence is optimized with a mixed-integer linear programming formulation while motion planning solves a whole-body optimal control problem considering the robot as a floating-base system. The loco-manipulation system automatically plans a full-coverage trajectory over multiple surfaces for contact-based non-destructive monitoring after unrolling the 3D-mesh region-of-interest selected from the user interface and projects it back to the surface. Our pipeline aims at offshore asset inspection and remote monitoring in industrial applications, and is also applicable in manufacturing and maintenance where area coverage is critical. We demonstrate the generality and scalability of our solution in a variety of robotic coverage path planning applications, including for multi-surface asset inspection using a quadrupedal manipulator.

[ pdf] [ DOI: 10.1109/CASE56687.2023.10260625]

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