This thesis studies the navigation task for autonomous UAVs to collect digital data in a risky environment. Three problem formulations are proposed according to different real-world situations. First, we focus on uniform probabilistic risk and assume UAV has unlimited amount of energy. With these assumptions, we provide the graph-based Data-collecting Robot Problem (DRP) model, and propose heuristic planning solutions that consist of a clustering step and a tour building step. Experiments show our methods provide high-quality solutions with high expected reward. Second, we investigate non-uniform probabilistic risk and limited energy capacity of UAV. We present the Data-collection Problem (DCP) to model the task. DCP is a grid-based Markov decision process, and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve data-collection efficiency, design four deep neural networks that model the opponent's behavior at different levels, and empirically prove that explicit opponent modeling with a dedicated network provides superior performance.
","abstract_html":"<p>This thesis studies the navigation task for autonomous UAVs to collect digital data in a risky environment. Three problem formulations are proposed according to different real-world situations. First, we focus on uniform probabilistic risk and assume UAV has unlimited amount of energy. With these assumptions, we provide the graph-based Data-collecting Robot Problem (DRP) model, and propose heuristic planning solutions that consist of a clustering step and a tour building step. Experiments show our methods provide high-quality solutions with high expected reward. Second, we investigate non-uniform probabilistic risk and limited energy capacity of UAV. We present the Data-collection Problem (DCP) to model the task. DCP is a grid-based Markov decision process, and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve data-collection efficiency, design four deep neural networks that model the opponent's behavior at different levels, and empirically prove that explicit opponent modeling with a dedicated network provides superior performance.</p>","abstract_has_math":false,"creators":["Xing, Zhi"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["Jae C. Oh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-25T07:00:00Z","date_published":"2017-08-25T07:00:00Z","updated_at":"2026-07-24T04:55:19Z","subjects":["Ensemble Method","Heuristic","Navigation","Opponent Modeling","Reinforcement Learning","Risk","Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/etd/776","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jae C. Oh"]},{"key":"dc:creator","label":"Author","values":["Xing, Zhi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ensemble Method","Heuristic","Navigation","Opponent Modeling","Reinforcement Learning","Risk","Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/etd/776"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis studies the navigation task for autonomous UAVs to collect digital data in a risky environment. Three problem formulations are proposed according to different real-world situations. First, we focus on uniform probabilistic risk and assume UAV has unlimited amount of energy. With these assumptions, we provide the graph-based Data-collecting Robot Problem (DRP) model, and propose heuristic planning solutions that consist of a clustering step and a tour building step. Experiments show our methods provide high-quality solutions with high expected reward. Second, we investigate non-uniform probabilistic risk and limited energy capacity of UAV. We present the Data-collection Problem (DCP) to model the task. DCP is a grid-based Markov decision process, and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve data-collection efficiency, design four deep neural networks that model the opponent's behavior at different levels, and empirically prove that explicit opponent modeling with a dedicated network provides superior performance.
"]},{"key":"dc:title","label":"Title","values":["Risk-aware navigation for UAV digital data collection"]}]}],"canonical_facts":{"dc:contributor":["Jae C. Oh"],"dc:creator":["Xing, Zhi"],"dc:description.abstract":["This thesis studies the navigation task for autonomous UAVs to collect digital data in a risky environment. Three problem formulations are proposed according to different real-world situations. First, we focus on uniform probabilistic risk and assume UAV has unlimited amount of energy. With these assumptions, we provide the graph-based Data-collecting Robot Problem (DRP) model, and propose heuristic planning solutions that consist of a clustering step and a tour building step. Experiments show our methods provide high-quality solutions with high expected reward. Second, we investigate non-uniform probabilistic risk and limited energy capacity of UAV. We present the Data-collection Problem (DCP) to model the task. DCP is a grid-based Markov decision process, and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve data-collection efficiency, design four deep neural networks that model the opponent's behavior at different levels, and empirically prove that explicit opponent modeling with a dedicated network provides superior performance.
"],"dc:identifier":["https://surface.syr.edu/etd/776"],"dc:subject":["Ensemble Method","Heuristic","Navigation","Opponent Modeling","Reinforcement Learning","Risk","Engineering"],"dc:title":["Risk-aware navigation for UAV digital data collection"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:55:19Z"}