2026-09-19T19:12:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1195132026-06-06T00:56:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Leslie Pack Kaelbling and Tomás Lozano-Pérez.
Axelrod, Brian Maxim
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-11T20:38:16Z
2018-12-11T20:38:16Z
2017
2017
http://hdl.handle.net/1721.1/119513
1066344450
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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 71-73).
As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We build a mathematical framework for analyzing the quality of estimated geometry, rigorously developing the notion of shadows. We then examine how to use these tools guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method's ability to evaluate if a trajectory or policy is safe. We also examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information.
by Brian Maxim Axelrod.
M.Eng.
73 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Algorithms for safe robot navigation
Thesis
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Publication
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<Abstract>As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We build a mathematical framework for analyzing the quality of estimated geometry, rigorously developing the notion of shadows. We then examine how to use these tools guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method's ability to evaluate if a trajectory or policy is safe. We also examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information.</Abstract>
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