Unifying Search-based and Compilation-based Approaches to Multi-agent Path Finding through Satisfiability Modulo Theories

Surynek, Pavel

doi:10.24963/ijcai.2019/164

Cited by 53 publications

(50 citation statements)

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“…Existing approaches to solve MAPF problems are categorised as optimal solvers, bounded-suboptimal solvers, prioritised solvers, rule-based solvers, and so on. Optimal solvers include Conflict Based Search (CBS) [19], Branch-and-Cut-and-Price (BCP) [20], A* based solvers [21] and Reduction Based Solvers [22]. These solvers solve the problem optimally and their weakness is the poor scalability.…”

Section: Multi-agent Path Findingmentioning

confidence: 99%

Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Chen

Alonso–Mora

Bai

et al. 2021

IEEE Robot. Autom. Lett.

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Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of robots is tasked with transporting a set of tasks, each from an initial location and each to a specified target location. Appearing in the context of automated warehouse logistics and automated mail sortation, MAPD requires first deciding which robot is assigned what task (i.e., Task Assignment or TA) followed by a subsequent coordination problem where each robot must be assigned collision-free paths so as to successfully complete its assignment (i.e., Multi-Agent Path Finding or MAPF). Leading methods in this area solve MAPD sequentially: first assigning tasks, then assigning paths. In this work we propose a new coupled method where task assignment choices are informed by actual delivery costs instead of by lower-bound estimates. The main ingredients of our approach are a marginal-cost assignment heuristic and a metaheuristic improvement strategy based on Large Neighbourhood Search. As a further contribution, we also consider a variant of the MAPD problem where each robot can carry multiple tasks instead of just one. Numerical simulations show that our approach yields efficient and timely solutions and we report significant improvement compared with other recent methods from the literature.

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Section: Multi-agent Path Findingmentioning

confidence: 99%

Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Chen

Alonso–Mora

Bai

et al. 2021

IEEE Robot. Autom. Lett.

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“…The objective of our empirical evaluation was to compare the performance of the different variants of our translation against representatives of search-based and SAT-based solvers. We compared to the publicly available SAT-based solver MDD-SAT [22] (enc=mdd), the SMT compilationbased solver SMT-CBS [17], and the search-based solver ICBS-h [9]. Our evaluation is focused on relatively small grids since grounding time grows too much for larger grids (e.g., 512 × 512), making the approach impractical.…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…We evaluate our approach on synthetic square grids and warehouse grids with an increasing number of agents. We compare against MDD-SAT [11], SMT-CBS [17] two stateof-the-art compilation-based solvers, and to ICBS-H [9], a representative of the state-of-the-art in search-based MAPF. We observe that our approach outperforms search-based solvers when agent congestion is high.…”

Section: Introductionmentioning

confidence: 99%

A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding

2021

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Multi-agent pathfinding (MAPF) is the problem of finding k non-colliding paths connecting k given initial positions with k given goal positions on a given map. In its sum-of-costs variant, the total number of moves and wait actions performed by agents before they definitely reach the goal is minimized. Not surprisingly, since MAPF is combinatorial, a number of compilations to Boolean Satisfiability (SAT) and Answer Set Programming (ASP) exist. In this paper, we describe in detail the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Compared to existing ASP compilations, a distinguishing feature of our compilation is that the number of total clauses (after grounding) grow linearly with the number of agents, while existing compilations grow quadratically. In addition, the optimization objective is such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search-based sum-of-costs MAPF solvers when grids are congested with agents. We also show that our approach is competitive with a SAT-based approach when follow conflicts are taken into account. We also explore the potential of our solver when finding makespanoptimal solutions, in which makespan is minimized first and then cost is minimized. Our results show that makespan-optimal solutions are slightly suboptimal in most benchmarks. Moreover, our MAPF solver, when run in that mode, is faster and scales better.

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“…The objective of our empirical evaluation was to compare the performance of the different variants of our translation against representatives of search-based and SAT-based solvers. We compared to the publicly available SAT-based solver MDD-SAT (Surynek 2014) (enc=mdd), the SMT compilation-based solver SMT-CBS (Surynek 2019), and the search-based solvers EPEA* (Goldenberg et al 2014), and ICBS-h (Felner et al 2018). Our evaluation is focused on relatively small grids since grounding time grows too much for larger grids (e.g., 512 × 512), making the approach impractical.…”

Section: Empirical Evaluationmentioning

confidence: 99%

“…We evaluate our approach on synthetic square grids and warehouse grids with an increasing number of agents. We compare against MDD-SAT ( Surynek et al 2016), SMT-CBS (Surynek 2019) two state-of-the-art compilation-based solvers, and iCBS-h (Felner et al 2018), a representative of the state-of-the-art in search-based MAPF. We observe that our approach outperforms both MDD-SAT and iCBSh when congestion is high.…”

Section: Introductionmentioning

confidence: 99%

Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming

Gómez

Hernández

Baier

2020

AAAI

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Solving a Multi-Agent Pathfinding (MAPF) problem involves finding non-conflicting paths that lead a number of agents to their goal location. In the sum-of-costs variant of MAPF, one is also required to minimize the total number of moves performed by agents before stopping at the goal. Not surprisingly, since MAPF is combinatorial, a number of compilations to Satisfiability solving (SAT) and Answer Set Programming (ASP) exist. In this paper, we propose the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Unlike existing compilations to ASP that we are aware of, our encoding is the first that, after grounding, produces a number of clauses that is linear on the number of agents. In addition, the representation of the optimization objective is also carefully written, such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search- and SAT-based sum-of-costs MAPF solvers when grids are congested with agents.

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Unifying Search-based and Compilation-based Approaches to Multi-agent Path Finding through Satisfiability Modulo Theories

Cited by 53 publications

References 1 publication

Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

A Compact Answer Set Programming Encoding of Multi-Agent Pathfinding

Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming

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