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Safety Assured Online Guidance for the Operations of Autonomous Systems Under Uncertainty.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Safety Assured Online Guidance for the Operations of Autonomous Systems Under Uncertainty./
作者:	Wu, Pengcheng.
面頁冊數:	1 online resource (168 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Robotics. -
電子資源:	click for full text (PQDT)
ISBN:	9798383086650

Safety Assured Online Guidance for the Operations of Autonomous Systems Under Uncertainty.
Wu, Pengcheng.

Safety Assured Online Guidance for the Operations of Autonomous Systems Under Uncertainty. - 1 online resource (168 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2024.

Includes bibliographical references

In this dissertation, we are going to develop safe, efficient motion planning algorithms for the operations of autonomous systems in uncertain environments. First, we assume that the uncertainties obey Gaussian distribution. We introduce a risk-bounded and fairness-aware path planning algorithm for multiple aircraft operating in such environments. This algorithm advances the sampling-based path planning with the risk domain formulation to generate stochastically safety assured collision-free paths that are robust to both vehicle and environmental obstacle uncertainties for multiple aircraft. Simulation studies demonstrate the promising performance of the proposed algorithm. Next, a safety assured decentralized online guidance algorithm with airborne self-separation capability is proposed and analyzed for multi-aircraft autonomous flight operations under uncertainties. The problem is formulated as a multi-agent Markov Decision Process with continuous action space and is solved by a customized decentralized online algorithm based on Monte Carlo Tree Search. A comprehensive numerical study shows that the proposed algorithm can provide safe onboard guidance with guaranteed low near mid-air collision probability in uncertain and high-density airspace environments. Given non-Gaussian uncertainties, we formulate a path planning problem for UAVs with those uncertainties and solve it through a chance-constrained optimization framework considering the convex approximation of the data-driven probabilistic reachable set. In this problem, the uncertainties of the model are converted to a convex approximation of a probabilistic reachable set and are then incorporated into the optimization framework. An efficient convex optimization algorithm is applied to solve the formulated optimization framework, and we achieve an optimal path for the UAV while ensuring the probability of collision is bounded by a threshold. Finally, numerical simulation validates the efficacy of our proposed approach.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383086650Subjects--Topical Terms:

561941
Robotics.
Subjects--Index Terms:

Planning algorithmsIndex Terms--Genre/Form:

554714
Electronic books.

Safety Assured Online Guidance for the Operations of Autonomous Systems Under Uncertainty.
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504 $a Includes bibliographical references
520 $a In this dissertation, we are going to develop safe, efficient motion planning algorithms for the operations of autonomous systems in uncertain environments. First, we assume that the uncertainties obey Gaussian distribution. We introduce a risk-bounded and fairness-aware path planning algorithm for multiple aircraft operating in such environments. This algorithm advances the sampling-based path planning with the risk domain formulation to generate stochastically safety assured collision-free paths that are robust to both vehicle and environmental obstacle uncertainties for multiple aircraft. Simulation studies demonstrate the promising performance of the proposed algorithm. Next, a safety assured decentralized online guidance algorithm with airborne self-separation capability is proposed and analyzed for multi-aircraft autonomous flight operations under uncertainties. The problem is formulated as a multi-agent Markov Decision Process with continuous action space and is solved by a customized decentralized online algorithm based on Monte Carlo Tree Search. A comprehensive numerical study shows that the proposed algorithm can provide safe onboard guidance with guaranteed low near mid-air collision probability in uncertain and high-density airspace environments. Given non-Gaussian uncertainties, we formulate a path planning problem for UAVs with those uncertainties and solve it through a chance-constrained optimization framework considering the convex approximation of the data-driven probabilistic reachable set. In this problem, the uncertainties of the model are converted to a convex approximation of a probabilistic reachable set and are then incorporated into the optimization framework. An efficient convex optimization algorithm is applied to solve the formulated optimization framework, and we achieve an optimal path for the UAV while ensuring the probability of collision is bounded by a threshold. Finally, numerical simulation validates the efficacy of our proposed approach.
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