TY - GEN
T1 - Comparison of Classic and Recent Multi-Agent Path Finding Methods via MAPFame
AU - Huang, Jiaqi
AU - Zhou, Yangming
AU - Zhou, Meng Chu
AU - Liu, Wei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - A Multi-Agent Path Finding (MAPF) problem aims to plan paths for multiple agents given a prescribed map and ensure they do not conflict with each other and travel the shortest distance or lowest cost in the minimal time. MAPF is useful in many practical applications, e.g., automated warehouses and intelligent factories. It has been widely-studied in the past decade. Existing MAPF algorithms have evolved from those solving single-agent path finding problems. When realized in different programming languages, they tend to deliver varying results regarding execution time and solution quality. Many kinds of simulated maps are used but some of them are not directly related to actual application environment. In this paper, we experimentally compare existing MAPF algorithms based on an open-source simulation platform called Multi-Agent Path Finding based on Advanced Methods and Evaluation (MAPFame). We analyze and test the effects of obstacle density, different maps, and agents counts on their performance indices. Hence, our research outcomes can be used by practitioners to select a right method for their particular applications.
AB - A Multi-Agent Path Finding (MAPF) problem aims to plan paths for multiple agents given a prescribed map and ensure they do not conflict with each other and travel the shortest distance or lowest cost in the minimal time. MAPF is useful in many practical applications, e.g., automated warehouses and intelligent factories. It has been widely-studied in the past decade. Existing MAPF algorithms have evolved from those solving single-agent path finding problems. When realized in different programming languages, they tend to deliver varying results regarding execution time and solution quality. Many kinds of simulated maps are used but some of them are not directly related to actual application environment. In this paper, we experimentally compare existing MAPF algorithms based on an open-source simulation platform called Multi-Agent Path Finding based on Advanced Methods and Evaluation (MAPFame). We analyze and test the effects of obstacle density, different maps, and agents counts on their performance indices. Hence, our research outcomes can be used by practitioners to select a right method for their particular applications.
KW - Conflict-Based Search
KW - Multi-Agent Path Finding
KW - Performance Comparison
KW - Robotic Simulation
UR - http://www.scopus.com/inward/record.url?scp=85179629318&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179629318&partnerID=8YFLogxK
U2 - 10.1109/ICNSC58704.2023.10319007
DO - 10.1109/ICNSC58704.2023.10319007
M3 - Conference contribution
AN - SCOPUS:85179629318
T3 - ICNSC 2023 - 20th IEEE International Conference on Networking, Sensing and Control
BT - ICNSC 2023 - 20th IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Networking, Sensing and Control, ICNSC 2023
Y2 - 25 October 2023 through 27 October 2023
ER -