EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal. In this paper, we study how to decrease its runtime even further using inadmissible heuristics. Motivated by Explicit Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS, that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high-level node to expand next. We also investigate recent improvements of CBS and adapt them to EECBS. We find that EECBS with the improvements runs significantly faster than the state-of-the-art bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of MAPF instances. We hope that the scalability of EECBS enables additional applications for bounded-suboptimal MAPF algorithms.Comment: Published at AAAI 202

A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that bypasses this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to further improve its performance. We prove that MO-CBS and its variants are able to compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms both MOA* as well as MOM*, a recently developed state-of-the-art multi-objective multi-agent planner.Comment: 11 pages, preliminary version published in ICRA 2021, journal version submitte

Improving Continuous-time Conflict Based Search

Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time. However, the scalability of CCBS is limited because it does not include any known improvements of CBS. In this paper, we begin to close this gap and explore how to adapt successful CBS improvements, namely, prioritizing conflicts (PC), disjoint splitting (DS), and high-level heuristics, to the continuous time setting of CCBS. These adaptions are not trivial, and require careful handling of different types of constraints, applying a generalized version of the Safe interval path planning (SIPP) algorithm, and extending the notion of cardinal conflicts. We evaluate the effect of the suggested enhancements by running experiments both on general graphs and $2^k$-neighborhood grids. CCBS with these improvements significantly outperforms vanilla CCBS, solving problems with almost twice as many agents in some cases and pushing the limits of multiagent path finding in continuous-time domains.Comment: This is a pre-print of the paper accepted to AAAI 202

Simulation of Centralized Algorithms for Multi-Agent Path Finding on Real Robots

Simulace řešení multi-agentího hledání cest je nezbytná pro výzkum, ale také pro demonstrace v akademickém prostředí. Většinou se simulace pouze zobrazuje na obrazovce bez použití robotických agentů. Používají-li se roboty, obdrží posloupnost příkazů, které potřebují provést, nebo příkazy obdrží postupně, aby správně sledovaly své naplánované cesty. Tato práce navrhuje nový přístup k simulaci centralizovaných multi-agentných algoritmů pro hledání cest na fyzických agentech s názvem ESO-Nav. V tomhle přístupu agenti nejsou součástí plánovacího procesu, ani nemají o svých cestách žádné informace. Agenti mají jednoduché předdefinované chování v prostředí, v kterém navigují na základě jeho podnetů. Pro skupinu robotů Ozobot Evo byl implementován funkční prototyp simulátoru, který využívá tento nový přístup.The simulation of multi-agent pathfinding solutions is essential for research but also in educational demonstrations. Most of the time, the simulation is only displayed on a screen without the use of robotic agents. If robots are used, they get a sequence of commands they need to execute, or they receive the commands gradually, to follow their planned paths correctly. This work proposes a novel approach to simulation of centralized multi-agent pathfinding algorithms on physical agents called ESO-Nav. In this approach, the agents are not part of the planning process, nor do they have any information about their paths. The agents have a simple predetermined behavior in an environment and navigate in it based on the environment outputs. A working prototype of a simulator that utilizes this novel approach was implemented for a group of Ozobot Evo robots