Sparsification for Fast Optimal Multi-Robot Path Planning in Lazy Compilation Schemes
This is an incremental improvement for robotics and AI planning, addressing efficiency in optimal path planning for multiple robots.
The paper tackles the problem of multi-robot path planning (MRPP) by enhancing a SAT-based algorithm through sparsification of candidate robot paths, resulting in smaller Boolean formulae that are constructed and solved faster while maintaining optimality guarantees.
Path planning for multiple robots (MRPP) represents a task of finding non-colliding paths for robots through which they can navigate from their initial positions to specified goal positions. The problem is usually modeled using undirected graphs where robots move between vertices across edges. Contemporary optimal solving algorithms include dedicated search-based methods, that solve the problem directly, and compilation-based algorithms that reduce MRPP to a different formalism for which an efficient solver exists, such as constraint programming (CP), mixed integer programming (MIP), or Boolean satisfiability (SAT). In this paper, we enhance existing SAT-based algorithm for MRPP via spartification of the set of candidate paths for each robot from which target Boolean encoding is derived. Suggested sparsification of the set of paths led to smaller target Boolean formulae that can be constructed and solved faster while optimality guarantees of the approach have been kept.