Ensuring Progress for Multiple Mobile Robots via Space Partitioning, Motion Rules, and Adaptively Centralized Conflict Resolution
This work addresses coordination challenges for multi-robot systems in domains like logistics or automation, offering a balanced solution between decentralized and centralized approaches, though it is incremental in combining existing ideas with new rules.
The paper tackles the problem of ensuring progress for multiple mobile robots in shared environments by introducing a framework that partitions space into regions and imposes motion rules with adaptive centralized conflict resolution, guaranteeing that all robots can reach their goals in finite time without requiring grid discretization or strong coordination assumptions.
In environments where multiple robots must coordinate in a shared space, decentralized approaches allow for decoupled planning at the cost of global guarantees, while centralized approaches make the opposite trade-off. These solutions make a range of assumptions - commonly, that all the robots share the same planning strategies. In this work, we present a framework that ensures progress for all robots without assumptions on any robot's planning strategy by (1) generating a partition of the environment into "flow", "open", and "passage" regions and (2) imposing a set of rules for robot motion in these regions. These rules for robot motion prevent deadlock through an adaptively centralized protocol for resolving spatial conflicts between robots. Our proposed framework ensures progress for all robots without a grid-like discretization of the environment or strong requirements on robot communication, coordination, or cooperation. Each robot can freely choose how to plan and coordinate for itself, without being vulnerable to other robots or groups of robots blocking them from their goals, as long as they follow the rules when necessary. We describe our space partition and motion rules, prove that the motion rules suffice to guarantee progress in partitioned environments, and demonstrate several cases in simulated polygonal environments. This work strikes a balance between each robot's planning independence and a guarantee that each robot can always reach any goal in finite time.