2026-09-18T22:18:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1014472022-01-13T07:53:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jonathan P. How.
Omidshafiei, Shayegan
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2016-03-03T20:29:09Z
2016-03-03T20:29:09Z
2015
2015
http://hdl.handle.net/1721.1/101447
939663644
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 129-139).
Planning, control, perception, and learning for multi-robot systems present signicant challenges. Transition dynamics of the robots may be stochastic, making it difficult to select the best action each robot should take at a given time. The observation model, a function of the robots' sensors, may be noisy or partial, meaning that deterministic knowledge of the team's state is often impossible to attain. Robots designed for real-world applications require careful consideration of such sources of uncertainty. This thesis contributes a framework for multi-robot planning in continuous spaces with partial observability. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems. However, representing and solving Dec-POMDPs is often intractable for large problems. This thesis extends the Dec-POMDP framework to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP), taking advantage of high- level representations that are natural for multi-robot problems. Dec-POSMDPs allow asynchronous decision-making, which is crucial in multi-robot domains. This thesis also presents algorithms for solving Dec-POSMDPs, which are more scalable than previous methods due to use of closed-loop macro-actions in planning. The proposed framework's performance is evaluated in a constrained multi-robot package delivery domain, showing its ability to provide high-quality solutions for large problems. Due to the probabilistic nature of state transitions and observations, robots operate in belief space, the space of probability distributions over all of their possible states. This thesis also contributes a hardware platform called Measurable Augmented Reality for Prototyping Cyber-Physical Systems (MAR-CPS). MAR-CPS allows real-time visualization of the belief space in laboratory settings.
by Shayegan Omidshafiei.
S.M.
139 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Decentralized control of multi-robot systems using partially observable Markov Decision Processes and belief space macro-actions
Thesis
application/pdf
Publication
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<Abstract>Planning, control, perception, and learning for multi-robot systems present signicant challenges. Transition dynamics of the robots may be stochastic, making it difficult to select the best action each robot should take at a given time. The observation model, a function of the robots' sensors, may be noisy or partial, meaning that deterministic knowledge of the team's state is often impossible to attain. Robots designed for real-world applications require careful consideration of such sources of uncertainty. This thesis contributes a framework for multi-robot planning in continuous spaces with partial observability. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems. However, representing and solving Dec-POMDPs is often intractable for large problems. This thesis extends the Dec-POMDP framework to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP), taking advantage of high- level representations that are natural for multi-robot problems. Dec-POSMDPs allow asynchronous decision-making, which is crucial in multi-robot domains. This thesis also presents algorithms for solving Dec-POSMDPs, which are more scalable than previous methods due to use of closed-loop macro-actions in planning. The proposed framework's performance is evaluated in a constrained multi-robot package delivery domain, showing its ability to provide high-quality solutions for large problems. Due to the probabilistic nature of state transitions and observations, robots operate in belief space, the space of probability distributions over all of their possible states. This thesis also contributes a hardware platform called Measurable Augmented Reality for Prototyping Cyber-Physical Systems (MAR-CPS). MAR-CPS allows real-time visualization of the belief space in laboratory settings.</Abstract>
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