407 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
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Conflict-Based Search is one of the most popular methods for multi-agent path
finding. Though it is complete and optimal, it does not scale well. Recent
works have been proposed to accelerate it by introducing various heuristics.
However, whether these heuristics can apply to non-grid-based problem settings
while maintaining their effectiveness remains an open question. In this work,
we find that the answer is prone to be no. To this end, we propose a
learning-based component, i.e., the Graph Transformer, as a heuristic function
to accelerate the planning. The proposed method is provably complete and
bounded-suboptimal with any desired factor. We conduct extensive experiments on
two environments with dense graphs. Results show that the proposed Graph
Transformer can be trained in problem instances with relatively few agents and
generalizes well to a larger number of agents, while achieving better
performance than state-of-the-art methods.Comment: Accepted by ICRA 202
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding
We propose a novel complete algorithm for multi-agent pathfinding (MAPF)
called lazy constraints addition search for MAPF (LaCAM). MAPF is a problem of
finding collision-free paths for multiple agents on graphs and is the
foundation of multi-robot coordination. LaCAM uses a two-level search to find
solutions quickly, even with hundreds of agents or more. At the low-level, it
searches constraints about agents' locations. At the high-level, it searches a
sequence of all agents' locations, following the constraints specified by the
low-level. Our exhaustive experiments reveal that LaCAM is comparable to or
outperforms state-of-the-art sub-optimal MAPF algorithms in a variety of
scenarios, regarding success rate, planning time, and solution quality of
sum-of-costs.Comment: to be presented at AAAI-2
Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding
We formalize and study the multi-goal task assignment and path finding
(MG-TAPF) problem from theoretical and algorithmic perspectives. The MG-TAPF
problem is to compute an assignment of tasks to agents, where each task
consists of a sequence of goal locations, and collision-free paths for the
agents that visit all goal locations of their assigned tasks in sequence.
Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally.
We present algorithms that build upon algorithmic techniques for the
multi-agent path finding problem and solve the MG-TAPF problem optimally and
bounded-suboptimally. We experimentally compare these algorithms on a variety
of different benchmark domains.Comment: ICRA 202
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
Anytime multi-agent path finding (MAPF) is a promising approach to scalable
path optimization in large-scale multi-agent systems. State-of-the-art anytime
MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution
is iteratively optimized by destroying and repairing a fixed number of parts,
i.e., the neighborhood, of the solution, using randomized destroy heuristics
and prioritized planning. Despite their recent success in various MAPF
instances, current LNS-based approaches lack exploration and flexibility due to
greedy optimization with a fixed neighborhood size which can lead to low
quality solutions in general. So far, these limitations have been addressed
with extensive prior effort in tuning or offline machine learning beyond actual
planning. In this paper, we focus on online learning in LNS and propose
Bandit-based Adaptive LArge Neighborhood search Combined with Exploration
(BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the
selection of destroy heuristics and neighborhood sizes on the fly during
search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and
empirically demonstrate cost improvements of at least 50% compared to
state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson
Sampling performs particularly well compared to alternative multi-armed bandit
algorithms.Comment: Accepted to AAAI 202
Fault-Tolerant Offline Multi-Agent Path Planning
We study a novel graph path planning problem for multiple agents that may
crash at runtime, and block part of the workspace. In our setting, agents can
detect neighboring crashed agents, and change followed paths at runtime. The
objective is then to prepare a set of paths and switching rules for each agent,
ensuring that all correct agents reach their destinations without collisions or
deadlocks, despite unforeseen crashes of other agents. Such planning is
attractive to build reliable multi-robot systems. We present problem
formalization, theoretical analysis such as computational complexities, and how
to solve this offline planning problem.Comment: to be presented at AAAI-2
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 -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
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