Moving Target Search with Environment Abstraction
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Moving Target Search (MTS) is a dynamic path planning problem, where an agent is trying to reach a moving entity with a minimum path cost. Problems of this nature can be found in video games and dynamic robotics, which require fast processing time (real time). In this thesis, we introduce a new algorithm for this problem - the Moving Target Search with Subgoal Graphs (MTSub). MTSub is based on environment abstraction and uses Subgoal Graphs to speed up searches without giving up cost minimal paths. The algorithm is optimal with respect to the knowledge that the agent has during the search. Experimental results show that MTSub meets the requirement of real time performance (e.g., 5 microseconds per step). Compared to G-FRA*, which is the best known dynamic algorithm so far, MTSub is up to 29 times faster in average time per step, and 186 times faster in maximum time per step.
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Copyright © 2015 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2015
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yorukcu-movingtargetsearchwithenvironmentabstraction.pdf | 2023-05-04 | Public | Download |