Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156705
Title: Cloud driven implementation for multi-agent path finding
Authors: Datta, Anusha
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Datta, A. (2022). Cloud driven implementation for multi-agent path finding. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156705
Abstract: Multi-Agent Path Finding (MAPF) is the computational problem of constructing collision-free paths for a set of agents from their respective start to goal positions within a given maze. In recent years, MAPF has gained increasing importance as it is central to many large-scale robotic applications, from logistic distribution systems to simultaneous localization and mapping. Over time, numerous approaches to MAPF have emerged, one of which is the dual level Conflict Based Search (CBS) Algorithm. At the high level, CBS performs search on a binary constraint tree. While at the lower level, it performs a search for a single agent at a time. In most cases, this reformulation enables CBS to examine fewer states than a global A* based approached, while still maintaining optimality. Hence, this project explores Conflict Based Search for optimal Multi-Agent Path Finding. These findings are augmented with additional experimentation on search performances of different lower level search heuristics. Furthermore, this project also includes the design, development and deployment of a cloud driven MAPF application. This application aims to provide an intuitive user experience to interact with the MAPF algorithm, visualise the traversal of the path finding solution and record statistical navigation parameters such as execution cost and execution time of the same. Finally, a navigation statistics pipeline is also established to produce strong predictive insights and navigation trends which subsequently facilitate intelligent business decisions.
URI: https://hdl.handle.net/10356/156705
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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