Multi-agent pathfinding (MAPF) is the problem of moving a group of agents to a set of target destinations while avoiding collisions. In this work, we study the online version of MAPF where new agents appear over time. Several variants of online MAPF are defined and analyzed theoretically, showing that it is not possible to create an optimal online MAPF solver. Nevertheless, we propose effective online MAPF algorithms that balance solution quality, runtime, and the number of plan changes an agent makes during execution.

The problem of Multi-Agent Path Finding (MAPF) is to find paths for a fixed set of agents from their current locations to some desired locations in such a way that the agents do not collide with each other. This problem has been extensively theoretically studied, frequently using an abstract model, that expects uniform durations of moving primitives and perfect synchronization of agents/robots. In this paper we study the question of how the abstract plans generated by existing MAPF algorithms perform in practice when executed on real robots, namely Ozobots. In particular, we use several abstract models of MAPF, including a robust version and a version that assumes turning of a robot, we translate the abstract plans to sequences of motion primitives executable on Ozobots, and we empirically compare the quality of plan execution (real makespan, the number of collisions).

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Multi-agent path finding (MAPF) deals with the problem of finding collision-free paths for a set of agents. Each agent moves from its start location to its destination location in a shared environment represented by a graph. Reduction-based solving approaches for MAPF, for example reduction to SAT, exploit a time-expended layered graph, where each layer corresponds to specific time. Hence, these approaches are natural for minimizing makespan (the shortest time till all agents reach their destinations). Modeling the other frequently used objective, namely Sum of Costs (SOC; sum of paths lengths of all agents) is more difficult as the solution with the smallest SOC may not be reached in the time-expended graph with the smallest makespan. In this paper we suggest two novel approaches to estimate the makespan, that guarantees existence of a SOC-optimal solution. The approaches are empirically compared with an existing reduction-based method as well as with the state-of-the-art search-based optimal MAPF solver.

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Multi-Agent Path Finding (MAPF) deals with the problem of finding collision-free paths for a set of agents moving in a shared environment while each agent has specified its destination. Colored MAPF generalizes MAPF by defining groups of agents that share a set of destination locations. In the paper, we evaluate several approaches to optimally solve the colored MAPF problem, namely, a method based on network flows, an extended version of conflict-based search, and two models using Boolean satisfiability. We also investigate methods for obtaining lower bounds on optimal solutions based on constraint and continuous relaxation techniques.