A Novel Warehouse Multi-Robot Automation System with Semi-Complete and Computationally Efficient Path Planning and Adaptive Genetic Task Allocation Algorithms

Tsang, Kam Fai Elvis; Ni, Yuqing; Wong, Cheuk Fung Raphael; Shi, Ling

doi:10.1109/icarcv.2018.8581092

Cited by 27 publications

(10 citation statements)

References 14 publications

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“…Moreover there is also practical motivation for such graphs. Floor-plans of many warehouses or shop-like spaces show similar structure where storage areas are surrounded by narrow aisles (Claes, Oliehoek, Baier, & Tuyls, 2017;Tsang, Ni, Wong, & Shi, 2018).…”

Section: Mdd-sat and The Makespan Objectivementioning

confidence: 99%

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Surynek¹,

Stern²,

Boyarski³

et al. 2022

jair

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In the multi-agent path finding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions, aiming to minimize a given objective function. Many MAPF solvers were introduced in the past decade for optimizing two specific objective functions: sum-of-costs and makespan. Two prominent categories of solvers can be distinguished: search-based solvers and compilation-based solvers. Search-based solvers were developed and tested for the sum-of-costs objective, while the most prominent compilation-based solvers that are built around Boolean satisfiability (SAT) were designed for the makespan objective. Very little is known on the performance and relevance of solvers from the compilation-based approach on the sum-of-costs objective. In this paper, we start to close the gap between these cost functions in the compilation-based approach. Our main contribution is a new SAT-based MAPF solver called MDD-SAT, that is directly aimed to optimally solve the MAPF problem under the sum-of-costs objective function. Using both a lower bound on the sum-of-costs and an upper bound on the makespan, MDD-SAT is able to generate a reasonable number of Boolean variables in our SAT encoding. We then further improve the encoding by borrowing ideas from ICTS, a search-based solver. In addition, we show that concepts applicable in search-based solvers like ICTS and ICBS are applicable in the SAT-based approach as well. Specifically, we integrate independence detection, a generic technique for decomposing an MAPF instance into independent subproblems, into our SAT-based approach, and we design a relaxation of our optimal SAT-based solver that results in a bounded suboptimal SAT-based solver. Experimental evaluation on several domains shows that there are many scenarios where our SAT-based methods outperform state-of-the-art sum-of-costs search-based solvers, such as variants of the ICTS and ICBS algorithms.

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Section: Mdd-sat and The Makespan Objectivementioning

confidence: 99%

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Surynek¹,

Stern²,

Boyarski³

et al. 2022

jair

View full text Add to dashboard Cite

show abstract

“…For this reason, most solution methods are approximate, i.e., they do not guarantee that solutions will have the minimum overall cost, but they are fast and provide solutions that are acceptable in practice. In this context, there are works that employ auction-based algorithms [25], heuristics [9,12,18], meta-heuristics [26,27,14] and hybrid algorithms [19,13,11], for example.…”

Section: Problem Descriptionmentioning

confidence: 99%

Efficient Task Allocation in Smart Warehouses with Multi-delivery Stations and Heterogeneous Robots

Oliveira¹,

Röning²,

Plentz³

et al. 2022

Preprint

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The task allocation problem in multi-robot systems (MRTA) is an NPhard problem whose viable solutions are usually found by heuristic algorithms. Considering the increasing need of improvement on logistics, the use of robots for increasing the efficiency of logistics warehouses is becoming a requirement. In a smart warehouse the main tasks consist of employing a fleet of automated picking and mobile robots that coordinate by picking up items from a set of orders from the shelves and dropping them at the delivery stations. Two aspects generally justify multi-robot task allocation complexity: (i) environmental aspects, such as multi-delivery stations and dispersed robots (since they remain in constant motion) and (ii) fleet heterogeneity, where robots' traffic speed and

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“…In many cases, an entire fleet of mobile robots cooperates to efficiently transport goods. These systems are typically all controlled in a centralized manner where one single computer coordinates the whole fleet, has global information, and uses optimization-based heuristics to find global solutions [1][2][3]. This central idea works well for small and simple systems but it lacks flexible manufacturing paradigms about (i) robustness: maintaining a high fault tolerance against uncertain environments (road blockage, device malfunction, etc.…”

Section: Introductionmentioning

confidence: 99%

Decentral task allocation for industrial AGV-systems with resource constraints

Ryck

Pissoort

Holvoet

et al. 2021

Journal of Manufacturing Systems

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A Novel Warehouse Multi-Robot Automation System with Semi-Complete and Computationally Efficient Path Planning and Adaptive Genetic Task Allocation Algorithms

Cited by 27 publications

References 14 publications

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Migrating Techniques from Search-based Multi-Agent Path Finding Solvers to SAT-based Approach

Efficient Task Allocation in Smart Warehouses with Multi-delivery Stations and Heterogeneous Robots

Decentral task allocation for industrial AGV-systems with resource constraints

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