Escaping Local Minima: Hybrid Artificial Potential Field with Wall-Follower for Decentralized Multi-Robot Navigation

We tackle the challenges of decentralized multi-robot navigation in environments with nonconvex obstacles, where complete environmental knowledge is unavailable. While reactive methods like Artificial Potential Field (APF) offer simplicity and efficiency, they suffer from local minima, causing robots to become trapped due to their lack of global environmental awareness. Other existing solutions either rely on inter-robot communication, are limited to single-robot scenarios, or struggle to overcome nonconvex obstacles effectively. Our proposed methods enable collision-free navigation using only local sensor and state information without a map. By incorporating a wall-following (WF) behavior into the APF approach, our method allows robots to escape local minima, even in the presence of nonconvex and dynamic obstacles including other robots. We introduce two algorithms for switching between APF and WF: a rule-based system and an encoder network trained on expert demonstrations. Experimental results show that our approach achieves substantially higher success rates compared to state-of-the-art methods, highlighting its ability to overcome the limitations of local minima in complex environments

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.