{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/7551"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/7551","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Deep Learning-Based Exploration Path Planning","abstract":"In this thesis, two deep learning-based path planning methods for autonomous exploration of subterranean environments using aerial robots are presented. One approach utilizes imitation learning, where training samples are generated by a sampling-based state of the art exploration path planner, to construct a model which proposes comparable trajectories to those of the expert planner in many underground tunnel environments. This imitation learning based method uses a small window of recent LiDAR measurements to infer trajectories at a fraction of the computational cost of the expert training planner while also removing the requirement for an online map reconstruction of the environment. The second proposed approach utilizes a deep reinforcement learning algorithm applicable to continuous state and action spaces and partially observed Markov decision processes; the reward for the agent is contingent upon the agent's efficient exploration of the environment. The proposed methods are evaluated in simulated and real-world environments.","abstract_html":"In this thesis, two deep learning-based path planning methods for autonomous exploration of subterranean environments using aerial robots are presented. One approach utilizes imitation learning, where training samples are generated by a sampling-based state of the art exploration path planner, to construct a model which proposes comparable trajectories to those of the expert planner in many underground tunnel environments. This imitation learning based method uses a small window of recent LiDAR measurements to infer trajectories at a fraction of the computational cost of the expert training planner while also removing the requirement for an online map reconstruction of the environment. The second proposed approach utilizes a deep reinforcement learning algorithm applicable to continuous state and action spaces and partially observed Markov decision processes; the reward for the agent is contingent upon the agent's efficient exploration of the environment. The proposed methods are evaluated in simulated and real-world environments.","abstract_has_math":false,"creators":["Reinhart, Russell E"],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Alexis, Konstantinos"],"committee_chairs":[],"committee_members":["Hand, Emily","Schmidt, Deena"],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-27T21:46:30Z","subjects":["Aerial Robotics","Autonomous Systems","Deep Learning","Path Planning"],"languages":[],"rights":["Creative Commons Attribution 4.0 United States"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/7551","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Alexis, Konstantinos"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Hand, Emily","Schmidt, Deena"]},{"key":"dc:creator","label":"Author","values":["Reinhart, Russell E"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-09-09T01:06:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-09-09T01:06:17Z"]},{"key":"dc:date.issued","label":"Date","values":["2020"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Aerial Robotics","Autonomous Systems","Deep Learning","Path Planning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution 4.0 United States"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/7551"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, two deep learning-based path planning methods for autonomous exploration of subterranean environments using aerial robots are presented. One approach utilizes imitation learning, where training samples are generated by a sampling-based state of the art exploration path planner, to construct a model which proposes comparable trajectories to those of the expert planner in many underground tunnel environments. This imitation learning based method uses a small window of recent LiDAR measurements to infer trajectories at a fraction of the computational cost of the expert training planner while also removing the requirement for an online map reconstruction of the environment. The second proposed approach utilizes a deep reinforcement learning algorithm applicable to continuous state and action spaces and partially observed Markov decision processes; the reward for the agent is contingent upon the agent's efficient exploration of the environment. 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This imitation learning based method uses a small window of recent LiDAR measurements to infer trajectories at a fraction of the computational cost of the expert training planner while also removing the requirement for an online map reconstruction of the environment. The second proposed approach utilizes a deep reinforcement learning algorithm applicable to continuous state and action spaces and partially observed Markov decision processes; the reward for the agent is contingent upon the agent's efficient exploration of the environment. 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