Decentralized Multi-Robot Task Allocation With Cooperative and Time-Extended Tasks
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| Izdano u: | ProQuest Dissertations and Theses (2025) |
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| 001 | 3271175222 | ||
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Teoh, Yee Shen | |
| 245 | 1 | |a Decentralized Multi-Robot Task Allocation With Cooperative and Time-Extended Tasks | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a This thesis explores a decentralized multi-robot task scheduling problem in which a heterogeneous group of energy-constrained robots collaborates to complete tasks with varying capability requirements and cross-schedule dependencies. We introduce a framework along with three distributed algorithms, coined Greedy plus Wiggle Scheduling (G+WS), Monte Carlo plus Wiggle Scheduling (MC+WS), and Q-Learning plus Wiggle Scheduling (QL+WS), which utilize a task allocation approach termed wiggle scheduling to allocate tasks to robots. To benchmark performance, the proposed algorithms are compared against a centralized mathematical optimization solution implemented using Gurobi. Experimental evaluations demonstrate that the MC+WS algorithms consistently produce better solution rewards than the other distributed algorithms and can produce solution rewards at 95% average of optimal solution while having a significantly faster algorithm run time compared to the centralized solutions, but are still slower than the other distributed algorithms and lack scalability. The G+WS algorithm yields the lowest solution rewards, at 86% average of optimal, but consistently achieves the fastest algorithm run time and demonstrates strong scalability as the number of tasks and robots increases. The QL+WS algorithm offers a balanced trade-off, requiring less algorithm run time than MC+WS while producing better solution rewards than G+WS, at 89% of optimal. | |
| 653 | |a Robotics | ||
| 653 | |a Computer engineering | ||
| 653 | |a Computer science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3271175222/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3271175222/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |