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Integer programming (IP) has proven to be highly effective in solving manypath-based optimization problems in robotics. However, the applications of IPare generally done in an ad-hoc, problem specific manner. In this work, afterexamined a wide range of path-based optimization problems, we describe an IPsolution methodology for these problems that is both easy to apply (in twosimple steps) and high-performance in terms of the computation time and theachieved optimality. We demonstrate the generality of our approach through theapplication to three challenging path-based optimization problems: multi-robotpath planning (MPP), minimum constraint removal (MCR), and reward collectionproblems RCPs). Associated experiments show that the approach can efficientlyproduce (near-)optimal solutions for problems with large state spaces, complexconstraints, and complicated objective functions. In conjunction with theproposition of the IP methodology, we introduce two new and practical roboticsproblems: multi-robot minimum constraint removal (MMCR) and multi-robot pathplanning (MPP) with partial solutions, which can be quickly and effectivelysolved using our proposed IP solution pipeline.

Integer Programming as a General Solution Methodology for Path-Based Optimization in Robotics: Principles, Best Practices, and Applications