7,933 research outputs found
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
Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search
Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent
path finding. At the high level, CBS repeatedly detects conflicts and resolves
one of them by splitting the current problem into two subproblems. Previous
work chooses the conflict to resolve by categorizing the conflict into three
classes and always picking a conflict from the highest-priority class. In this
work, we propose an oracle for conflict selection that results in smaller
search tree sizes than the one used in previous work. However, the computation
of the oracle is slow. Thus, we propose a machine-learning framework for
conflict selection that observes the decisions made by the oracle and learns a
conflict-selection strategy represented by a linear ranking function that
imitates the oracle's decisions accurately and quickly. Experiments on
benchmark maps indicate that our method significantly improves the success
rates, the search tree sizes and runtimes over the current state-of-the-art CBS
solver
Pathfinding in Games
Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
We present a scalable and effective multi-agent safe motion planner that
enables a group of agents to move to their desired locations while avoiding
collisions with obstacles and other agents, with the presence of rich
obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation
limits, and disturbances. We address this problem by finding a piecewise linear
path for each agent such that the actual trajectories following these paths are
guaranteed to satisfy the reach-and-avoid requirement. We show that the spatial
tracking error of the actual trajectories of the controlled agents can be
pre-computed for any qualified path that considers the minimum duration of each
path segment due to actuation limits. Using these bounds, we find a
collision-free path for each agent by solving Mixed Integer-Linear Programs and
coordinate agents by using the priority-based search. We demonstrate our method
by benchmarking in 2D and 3D scenarios with ground vehicles and quadrotors,
respectively, and show improvements over the solving time and the solution
quality compared to two state-of-the-art multi-agent motion planners.Comment: Accepted at AAAI2021. 9 pages, 5 figures, 1 tabl
Proceedings of the 2022 XCSP3 Competition
This document represents the proceedings of the 2022 XCSP3 Competition. The
results of this competition of constraint solvers were presented at FLOC
(Federated Logic Conference) 2022 Olympic Games, held in Haifa, Israel from
31th July 2022 to 7th August, 2022.Comment: arXiv admin note: text overlap with arXiv:1901.0183
Engineering LaCAM: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding
This paper addresses the challenges of real-time, large-scale, and
near-optimal multi-agent pathfinding (MAPF) through enhancements to the
recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm
that guarantees the eventual finding of optimal solutions for cumulative
transition costs. While it has demonstrated remarkable planning success rates,
surpassing various state-of-the-art MAPF methods, its initial solution quality
is far from optimal, and its convergence speed to the optimum is slow. To
overcome these limitations, this paper introduces several improvement
techniques, partly drawing inspiration from other MAPF methods. We provide
empirical evidence that the fusion of these techniques significantly improves
the solution quality of LaCAM*, thus further pushing the boundaries of MAPF
algorithms.Comment: 20 page
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