Incremental CBS: Adaptive Multi-Agent Pathfinding in Dynamic Environments

In Multi-Agent Pathfinding (MAPF), traditional Conflict-Based Search (CBS) algorithms excel in static environments but struggle with efficiency and responsiveness in dynamic settings. This study introduces the Incremental Conflict-Based Search (ICBS) algorithm, which enhances response efficiency through incremental updates, only adjusting paths for agents affected by environmental changes. By constructing a dependency graph between agent paths and the environment, ICBS quickly identifies necessary path adjustments. A dynamic monitoring module ensures timely responses to obstacle changes. Experimental results show that ICBS outperforms traditional CBS in dynamic scenarios, making it ideal for applications like automated warehouse management and robotic soccer.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/rcae62637.2024.10834259
Citations
1
Primary Topic
Multi-Agent Systems and Negotiation
Type
article
Field-Weighted Citation Impact
0.35
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article

Incremental CBS: Adaptive Multi-Agent Pathfinding in Dynamic Environments

Buyun Wang, Jun Cheng, Xichen Xing
1 citations
Multi-Agent Systems and Negotiation
0.35
article

Incremental CBS: Adaptive Multi-Agent Pathfinding in Dynamic Environments

Buyun Wang, Jun Cheng, Xichen Xing
article en
1 citations

Abstract

In Multi-Agent Pathfinding (MAPF), traditional Conflict-Based Search (CBS) algorithms excel in static environments but struggle with efficiency and responsiveness in dynamic settings. This study introduces the Incremental Conflict-Based Search (ICBS) algorithm, which enhances response efficiency through incremental updates, only adjusting paths for agents affected by environmental changes. By constructing a dependency graph between agent paths and the environment, ICBS quickly identifies necessary path adjustments. A dynamic monitoring module ensures timely responses to obstacle changes. Experimental results show that ICBS outperforms traditional CBS in dynamic scenarios, making it ideal for applications like automated warehouse management and robotic soccer.

Anhui Polytechnic University (CN)
Openalex Percentile: Top 30%
Multi-Agent Systems and Negotiation
0.35
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Incremental CBS: Adaptive Multi-Agent Pathfinding in Dynamic Environments — Buyun Wang, Jun Cheng, et al. · (2024) | TGRS Research Map | TGRS