Date of Award

2022

Document Type

Dissertation

Degree Name

Ph.D.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science

First Advisor

Nathan R. Sturtevant

Second Advisor

Scott T. Leutenegger

Third Advisor

Matthew J. Rutherford

Keywords

Artificial intelligence, Automated planning, Heuristic search, Multi-agent systems, Optimization, Robotics

Abstract

In the multi-agent pathfinding (MAPF) problem, agents must move from their current locations to their individual destinations while avoiding collisions. Ideally, agents move to their destinations as quickly and efficiently as possible. MAPF has many real-world applications such as navigation, warehouse automation, package delivery and games. Coordination of agents is necessary in order to avoid conflicts, however, it can be very computationally expensive to find mutually conflict-free paths for multiple agents – especially as the number of agents is increased. Existing state-ofthe- art algorithms have been focused on simplified problems on grids where agents have no shape or volume, and each action executed by the agents have the same duration, resulting in simplified collision detection and synchronous, timed execution. In the real world agents have a shape, and usually execute actions with variable duration. This thesis re-formulates the MAPF problem definition for continuous actions, designates specific techniques for continuous-time collision detection, re-formulates two popular algorithms for continuous actions and formulates a new algorithm called Conflict-Based Increasing Cost Search (CBICS) for continuous actions.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Thayne T. Walker

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

261 pgs

Discipline

Artificial intelligence, Robotics, Computer science

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