Improved CBS algorithm based on the idea of pre-planning

The Conflict-Based Search (CBS) algorithm is one of the Multi-Agent Path Finding (MAPF) algorithms that has received much attention in recent years. It is a two-level algorithm. The high-level detects and resolves conflicts on a Constraint Tree (CT). Its low-level algorithm is to plan the optimal pa...

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Bibliographic Details
Main Authors Wang, Zhuoran, Wen, Jiayan, Lu, Jingjing
Format Conference Proceeding
LanguageEnglish
Published SPIE 26.05.2023
Online AccessGet full text
ISBN9781510666160
1510666168
ISSN0277-786X
DOI10.1117/12.2682558

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Summary:The Conflict-Based Search (CBS) algorithm is one of the Multi-Agent Path Finding (MAPF) algorithms that has received much attention in recent years. It is a two-level algorithm. The high-level detects and resolves conflicts on a Constraint Tree (CT). Its low-level algorithm is to plan the optimal path for a given agent that satisfies all the constraints of the agent. In this paper, the Space-Time A* (STA*) algorithm is used as the low-level algorithm, which adds the time dimension to the A* algorithm. It means that the planning path for a single agent tends to generate and extend more and more sub-nodes. As the complexity of the problem increases, the efficiency of the low-level solution becomes an increasingly large part of the overall solution efficiency. This leads to an inefficient overall solution. To address this problem, an improved CBS algorithm is proposed. This algorithm is used when the current node in CT has conflicts. It uses current node information and Multi-value Decision Diagram (MDD) technology to pre-plan before formal re-planning. The pre-planning phase entails obtaining information pertaining to the path that satisfies the new constraint, which is referred to as pre-planning information. During the formal re-planning, the pre-planning information is used to guide the low-level algorithm to select the generated sub-nodes. The simulation experiments show that CBS with pre-planning improves the solution time by 24.4% and reduces the search space by 38.7%.
Bibliography:Conference Date: 2023-03-17|2023-03-19
Conference Location: Nanchang, China
ISBN:9781510666160
1510666168
ISSN:0277-786X
DOI:10.1117/12.2682558