Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

HGHA: task allocation and path planning for warehouse agents

HGHA: task allocation and path planning for warehouse agents With the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system.Design/methodology/approachThe TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise.FindingsExperiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots.Originality/valueThis paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Assembly Automation Emerald Publishing

HGHA: task allocation and path planning for warehouse agents

Liu, Yandong; Han, Dong; Wang, Lujia; Xu, Cheng-Zhong
Assembly Automation , Volume 41 (2): 9 – Jul 27, 2021
Download PDF
9 pages

Loading next page...
 
/lp/emerald-publishing/hgha-task-allocation-and-path-planning-for-warehouse-agents-MHM4V0QL7W
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0144-5154
DOI
10.1108/aa-10-2020-0152
Publisher site
See Article on Publisher Site

Abstract

With the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system.Design/methodology/approachThe TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise.FindingsExperiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots.Originality/valueThis paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

Journal

Assembly Automation – Emerald Publishing

Published: Jul 27, 2021

Keywords: Path planning; Multi-agent; Task allocation; Hierarchical Genetic Highways Algorithm

References

  • A multi-agent system for distributed multi-project scheduling: an auction-based negotiation approach
    Adhau, S.; Mittal, M.; Mittal, A.
  • ACD 3 GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization
    Ayari, A.; Bouamama, S.
  • Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem
    Barer, M.; Sharon, G.; Stern, R.; Felner, A.
  • ODrM* optimal multirobot path planning in low dimensional search spaces
    Ferner, C.; Wagner, G.; Choset, H.
  • A market-based task allocation framework for autonomous underwater surveillance networks
    Ferri, G.; Lepage, K.
  • A formal analysis and taxonomy of task allocation in multi-robot systems
    Gerkey, B.P.; Matarić, M.J.
  • A novel swarm robot simulation platform for warehousing logistics
    Liu, Y.; Wang, L.; Huang, H.; Liu, M.; Xu, C.-Z.
  • Optimal target assignment and path finding for teams of agents
    Ma, H.; Koenig, S.
  • Evolutionary multi-objective air-conditioning schedule optimization for office buildings
    Ohta, Y.; Sato, H.
  • Conflict-based search for optimal multi-agent pathfinding
    Sharon, G.; Stern, R.; Felner, A.; Sturtevant, N.R.
  • Reduced time-expansion graphs and goal decomposition for solving cooperative path finding Sub-Optimally
    Surynek, P.
  • Fast and memory-efficient multi-agent pathfinding
    Wang, K.-H.C.; Botea, A.
  • Real-time multisensor data retrieval for cloud robotic systems
    Wang, L.; Liu, M.; Meng, M.Q.-H.
  • Spectrum sharing in cognitive radio networks – an auction-based approach
    Wang, X.; Li, Z.; Xu, P.; Xu, Y.; Gao, X.; Chen, H.-H.
  • A descriptor system approach for estimation of incipient faults with application to high-speed railway traction devices
    Wu, Y.; Jiang, B.; Lu, N.
  • Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains
    Wu, Y.; Jiang, B.; Wang, Y.
  • Coordinating hundreds of cooperative, autonomous vehicles in warehouses
    Wurman, P.R.; D'Andrea, R.; Mountz, M.
  • Cooperative multi-agent search using Bayesian approach with connectivity maintenance
    Xiao, H.; Cui, R.; Xu, D.
  • Multi-agent path planning and network flow
    Yu, J.; Lavalle, S.M.
  • Planning optimal paths for multiple robots on graphs
    Yu, J.; Lavalle, S.M.
  • Motion removal for reliable RGB-D SLAM in dynamic environments
    Yuxiang, S.; Ming, L.; Meng, Q.-H.
  • A mobile robot exploration algorithm
    Zelinsky, A.