research-article

Public Access

Task and Path Planning for Multi-Agent Pickup and Delivery

Authors:

Sven KoenigAuthors Info & Claims

AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems

Pages 1152 - 1160

Published: 08 May 2019 Publication History

Abstract

We study the offline Multi-Agent Pickup-and-Delivery (MAPD) problem, where a team of agents has to execute a batch of tasks with release times in a known environment. To execute a task, an agent has to move first from its current location to the pickup location of the task and then to the delivery location of the task. The MAPD problem is to assign tasks to agents and plan collision-free paths for them to execute their tasks. Online MAPD algorithms can be applied to the offline MAPD problem, but do not utilize all of the available information and may thus not be effective. Therefore, we present two novel offline MAPD algorithms that improve a state-of-the-art online MAPD algorithm with respect to task planning, path planning, and deadlock avoidance for the offline MAPD problem. Our MAPD algorithms first compute one task sequence for each agent by solving a special traveling salesman problem and then plan paths according to these task sequences. We also introduce an effective deadlock avoidance method, called "reserving dummy paths.'' Theoretically, our MAPD algorithms are complete for well-formed MAPD instances, a realistic subclass of all MAPD instances. Experimentally, they produce solutions of smaller makespans and scale better than the online MAPD algorithm in simulated warehouses with hundreds of robots and thousands of tasks.

References

[1]

T. Bektas. 2006. The Multiple Traveling Salesman Problem: an Overview of Formulations and Solution Procedures. Omega, Vol. 34, 3 (2006), 209--219.

[2]

E. Boyarski, A. Felner, R. Stern, G. Sharon, D. Tolpin, O. Betzalel, and S. E. Shimony. 2015. ICBS: Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding. In IJCAI . 740--746.

Digital Library

[3]

R. W. Calvo and A. Colorni. 2007. An Effective and Fast Heuristic for the Dial-a-Ride Problem. 4OR, Vol. 5, 1 (2007), 61--73.

[4]

A. Das, S. Gollapudi, A. Kim, D. Panigrahi, and C. Swamy. 2018. Minimizing Latency in Online Ride and Delivery Services. In WWW. 379--388.

Digital Library

[5]

E. Erdem, D. G. Kisa, U. Oztok, and P. Schueller. 2013. A General Formal Framework for Pathfinding Problems with Multiple Agents. In AAAI . 290--296.

Digital Library

[6]

A. Felner, R. Stern, S. E. Shimony, E. Boyarski, M. Goldenberg, G. Sharon, N. R. Sturtevant, G. Wagner, and P. Surynek. 2017. Search-Based Optimal Solvers for the Multi-Agent Pathfinding Problem: Summary and Challenges. In SoCS. 29--37.

[7]

M. Goldenberg, A. Felner, R. Stern, G. Sharon, N. R. Sturtevant, R. C. Holte, and J. Schaeffer. 2014. Enhanced Partial Expansion A* . Journal of Artificial Intelligence Research, Vol. 50 (2014), 141--187.

Digital Library

[8]

K. Helsgaun. 2017. An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems . Technical Report. Roskilde University.

[9]

W. Hönig, T. K. S. Kumar, H. Ma, N. Ayanian, and S. Koenig. 2016. Formation Change for Robot Groups in Occluded Environments. In IROS . 4836--4842.

[10]

R. Luna and K. E. Bekris. 2011. Push and Swap: Fast Cooperative Path-Finding with Completeness Guarantees. In IJCAI . 294--300.

Digital Library

[11]

H. Ma and S. Koenig. 2016. Optimal Target Assignment and Path Finding for Teams of Agents. In AAMAS . 1144--1152.

Digital Library

[12]

H. Ma, S. Koenig, N. Ayanian, L. Cohen, W. Hönig, T. K. S. Kumar, T. Uras, H. Xu, C. Tovey, and G. Sharon. 2016a. Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios. In IJCAI-16 Workshop on Multi-Agent Path Finding .

[13]

H. Ma, J. Li, T. K. S. Kumar, and S. Koenig. 2017a. Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks. In AAMAS . 837--845.

Digital Library

[14]

H. Ma, C. Tovey, G. Sharon, T. K. S. Kumar, and S. Koenig. 2016b. Multi-Agent Path Finding with Payload Transfers and the Package-Exchange Robot-Routing Problem. In AAAI. 3166--3173.

Digital Library

[15]

H. Ma, J. Yang, L. Cohen, T. K. S. Kumar, and S. Koenig. 2017b. Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments. In AIIDE . 270--272.

[16]

P. MacAlpine, E. Price, and P. Stone. 2014. SCRAM: Scalable Collision-Avoiding Role Assignment with Minimal-Makespan for Formational Positioning. In AAMAS. 1463--1464.

Digital Library

[17]

R. Morris, C. Pasareanu, K. Luckow, W. Malik, H. Ma, S. Kumar, and S. Koenig. 2016. Planning, Scheduling and Monitoring for Airport Surface Operations. In AAAI-16 Workshop on Planning for Hybrid Systems .

[18]

V. Nguyen, P. Obermeier, T. C. Son, T. Schaub, and W. Yeoh. 2017. Generalized Target Assignment and Path Finding using Answer Set Programming. In IJCAI . 1216--1223.

Digital Library

[19]

E. Nunes, M. Manner, H. Mitiche, and M. Gini. 2017. A Taxonomy for Task Allocation Problems with Temporal and Ordering Constraints. Robotics and Autonomous Systems, Vol. 90 (2017), 55--70.

Digital Library

[20]

E. Osaba, F. Diaz, E. Onieva, P. López-Garc'ia, R. Carballedo, and A. Perallos. 2015. A Parallel Meta-Heuristic for Solving a Multiple Asymmetric Traveling Salesman Problem with Simulateneous Pickup and Delivery Modeling Demand Responsive Transport Problems. In International Conference on Hybrid Artificial Intelligence Systems . Springer, 557--567.

[21]

G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant. 2015. Conflict-Based Search for Optimal Multi-Agent Pathfinding. Artificial Intelligence, Vol. 219 (2015), 40--66.

Digital Library

[22]

G. Sharon, R. Stern, M. Goldenberg, and A. Felner. 2013. The Increasing Cost Tree Search for Optimal Multi-Agent Pathfinding. Artificial Intelligence, Vol. 195 (2013), 470--495.

Digital Library

[23]

T. S. Standley. 2010. Finding Optimal Solutions to Cooperative Pathfinding Problems. In AAAI . 173--178.

Digital Library

[24]

A. Stentz, M B. Dias, R. M. Zlot, and N. Kalra. 2004. Market-Based Approaches for Coordination of Multi-Robot Teams at Different Granularities of Interaction. In International Conference on Robotics and Remote Systems for Hazardous Environments .

[25]

N. R. Sturtevant and M. Buro. 2006. Improving Collaborative Pathfinding Using Map Abstraction. In AIIDE. 80--85.

Digital Library

[26]

P. Surynek. 2015. Reduced Time-Expansion Graphs and Goal Decomposition for Solving Cooperative Path Finding Sub-Optimally. In IJCAI. 1916--1922.

Digital Library

[27]

J. P. Van den Berg and M. H. Overmars. 2005. Prioritized Motion Planning for Multiple Robots. IROS. 430--435.

[28]

M. Veloso, J. Biswas, B. Coltin, and S. Rosenthal. 2015. CoBots: Robust Symbiotic Autonomous Mobile Service Robots. In IJCAI . 4423--4429.

Digital Library

[29]

G. Wagner and H. Choset. 2015. Subdimensional Expansion for Multirobot Path Planning. Artificial Intelligence, Vol. 219 (2015), 1--24.

Digital Library

[30]

K. Wang and A. Botea. 2011. MAPP: A Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees. Journal of Artificial Intelligence Research, Vol. 42 (2011), 55--90.

Digital Library

[31]

P. R. Wurman, R. D'Andrea, and M. Mountz. 2008. Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses. AI Magazine, Vol. 29, 1 (2008), 9--20.

Digital Library

[32]

S. Yoon and J. Kim. 2017. Efficient Multi-Agent Task Allocation for Collaborative Route Planning with Multiple Unmanned Vehicles. IFAC-PapersOnLine, Vol. 50, 1 (2017), 3580--3585.

[33]

J. Yu and S. M. LaValle. 2013a. Multi-Agent Path Planning and Network Flow. WAFR, E. Frazzoli, T. Lozano-Perez, N. Roy, and D. Rus (Eds.). Vol. 86. Springer, 157--173.

[34]

J. Yu and S. M. LaValle. 2013b. Planning Optimal Paths for Multiple Robots on Graphs. In ICRA. 3612--3617.

[35]

J. Yu and S. M. LaValle. 2013c. Structure and Intractability of Optimal Multi-Robot Path Planning on Graphs. In AAAI . 1444--1449.

Digital Library

Cited By

Coscia M(2022)Generalized Euclidean Measure to Estimate Distances on Multilayer NetworksACM Transactions on Knowledge Discovery from Data10.1145/352939616:6(1-22)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1145/3529396
Choudhury SSolovey KKochenderfer MPavone M(2021)Efficient Large-Scale Multi-Drone Delivery using Transit NetworksJournal of Artificial Intelligence Research10.1613/jair.1.1245070(757-788)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1613/jair.1.12450
Andreychuk AYakovlev KAtzmon DSternr R(2019)Multi-agent pathfinding with continuous timeProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367039(39-45)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367039

Index Terms

Task and Path Planning for Multi-Agent Pickup and Delivery
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
    2. Planning and scheduling
      1. Multi-agent planning
      2. Robotic planning

Recommendations

Multi-Agent Pickup and Delivery with Task Deadlines
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

We study the multi-agent pickup and delivery problem with task deadlines, where a team of agents execute a batch of tasks with individual deadlines to maximize the number of tasks completed by their deadlines. Existing approaches to multi-agent pickup ...
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In ...
Automatic task scheduling optimization and collision-free path planning for multi-areas problem
Abstract
Automatic task scheduling and collision-free path planning for multi-task optimization is a great challenge in various industrial applications. It is a typical coupling problem between task sequence optimization and collision-free path planning. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

AAMAS '19: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems

May 2019

2518 pages

ISBN:9781450363099

General Chairs:
Edith Elkind
University of Oxford, UK
,
Manuela Veloso
CMU (on leave), JPMorgan, USA
,
Program Chairs:
Noa Agmon
Bar-Ilan University, Israel
,
Matthew E. Taylor
Borealis AI, Canada

Sponsors

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 08 May 2019

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation (NSF)
Amazon

Conference

AAMAS '19

Sponsor:

SIGAI

AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems

May 13 - 17, 2019

Montreal QC, Canada

Acceptance Rates

AAMAS '19 Paper Acceptance Rate 193 of 793 submissions, 24%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
1,523
Total Downloads

Downloads (Last 12 months)480
Downloads (Last 6 weeks)54

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Coscia M(2022)Generalized Euclidean Measure to Estimate Distances on Multilayer NetworksACM Transactions on Knowledge Discovery from Data10.1145/352939616:6(1-22)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1145/3529396
Choudhury SSolovey KKochenderfer MPavone M(2021)Efficient Large-Scale Multi-Drone Delivery using Transit NetworksJournal of Artificial Intelligence Research10.1613/jair.1.1245070(757-788)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1613/jair.1.12450
Andreychuk AYakovlev KAtzmon DSternr R(2019)Multi-agent pathfinding with continuous timeProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367039(39-45)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367039

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten