Integration of a Graph-Based Path Planner and Mixed-Integer MPC for Robot Navigation in Cluttered Environments
Year: 2025
Type: preprint
Abstract: The ability to update a path plan is a required capability for autonomous mobile robots navigating through uncertain environments. This paper proposes a re-planning strategy using a multilayer planning and control framework for cases where the robot's environment is partially known. A medial axis graph-based planner defines a global path plan based on known obstacles, where each edge in the graph corresponds to a unique corridor. A mixed-integer model predictive control (MPC) method detects if a terminal constraint derived from the global plan is infeasible, subject to a non-convex description of the local environment. Infeasibility detection is used to trigger efficient global re-planning via medial axis graph edge deletion. The proposed re-planning strategy is demonstrated experimentally.
Source: arXiv (Cornell University)
Authors: Robbins, Joshua A., Harnett, Stephen J., Thompson, Andrew F., Brennan, Sean, Pangborn, Herschel C.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979326120
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