Merry-Go-Round: Safe Control of Decentralized Multi-Robot Systems with Deadlock Prevention

We propose a hybrid approach for decentralized multi-robot navigation that ensures both safety and deadlock prevention. Building on a standard control formulation, we add a lightweight deadlock prevention mechanism by forming temporary "roundabouts" (circular reference paths). Each robot relies only on local, peer-to-peer communication and a controller for base collision avoidance; a roundabout is generated or joined on demand to avert deadlocks. Robots in the roundabout travel in one direction until an escape condition is met, allowing them to return to goal-oriented motion. Unlike classical decentralized methods that lack explicit deadlock resolution, our roundabout maneuver ensures system-wide forward progress while preserving safety constraints. Extensive simulations and physical robot experiments show that our method consistently outperforms or matches the success and arrival rates of other decentralized control approaches, particularly in cluttered or high-density scenarios, all with minimal centralized coordination.

Safe Interval RRT* for Scalable Multi-Robot Path Planning in Continuous Space

In this paper, we consider the problem of Multi-Robot Path Planning (MRPP) in continuous space to find conflict-free paths. The difficulty of the problem arises from two primary factors. First, the involvement of multiple robots leads to combinatorial decision-making, which escalates the search space exponentially. Second, the continuous space presents potentially infinite states and actions. For this problem, we propose a two-level approach where the low level is a sampling-based planner Safe Interval RRT* (SI-RRT*) that finds a collision-free trajectory for individual robots. The high level can use any method that can resolve inter-robot conflicts where we employ two representative methods that are Prioritized Planning (SI-CPP) and Conflict Based Search (SI-CCBS). Experimental results show that SI-RRT* can find a high-quality solution quickly with a small number of samples. SI-CPP exhibits improved scalability while SI-CCBS produces higher-quality solutions compared to the state-of-the-art planners for continuous space. Compared to the most scalable existing algorithm, SI-CPP achieves a success rate that is up to 94% higher with 100 robots while maintaining solution quality (i.e., flowtime, the sum of travel times of all robots) without significant compromise. SI-CPP also decreases the makespan up to 45%. SI-CCBS decreases the flowtime by 9% compared to the competitor, albeit exhibiting a 14% lower success rate.