{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84049"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84049","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables","abstract":"M.Eng.","abstract_html":"M.Eng.","abstract_has_math":false,"creators":["Gabani, Krushang; 0000-0001-5172-1703"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Chowdhury, Souma","Mechanical and Aerospace Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:24Z","date_published":"2022-06-21T15:47:24Z","updated_at":"2026-07-27T19:05:30Z","subjects":["mechanical engineering","robotics"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84049","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhury, Souma","Mechanical and Aerospace Engineering"]},{"key":"dc:creator","label":"Author","values":["Gabani, Krushang; 0000-0001-5172-1703"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:24Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mechanical engineering","robotics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84049"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.Eng.","This work focuses on the idea of generating the learning framework for cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy, they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. This work presents a learning-based approach to train such reciprocal maneuvers. A Neuroevolution approach, which uses evolutionary algorithms to optimize the topology and weights of neural networks simultaneously, is used as the learning method--which operates over a set of sample approach scenarios to evaluate the fitness of each neural network candidate. Unlike most existing work (that minimize travel distance), the training process here has the capability of minimizing the required detection range and detection time. The specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. This training process can also handle substantial challenges like uncertain UAV localization. For that, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain. These capabilities have essential practical implications w.r.t. alleviating the dependency on sophisticated sensing and their reliability under various environments. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burdensome under these uncertainties. For an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. Also, a surrogate model is used to estimate the energy consumption and minimum distance between UAVs. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables"]}]}],"canonical_facts":{"dc:contributor":["Chowdhury, Souma","Mechanical and Aerospace Engineering"],"dc:creator":["Gabani, Krushang; 0000-0001-5172-1703"],"dc:date":["2022-06-21T15:47:24Z","2020"],"dc:description":["M.Eng.","This work focuses on the idea of generating the learning framework for cooperative collision avoidance between two quadcopters. Two strategies for reciprocal online collision-avoiding actions (i.e., coherent maneuvers without requiring any real-time consensus) are proposed. In the first strategy, UAVs change their speed, while in the second strategy, they change their heading to avoid a collision. The avoidance actions are parameterized in terms of the time difference between detecting the collision and starting the maneuver and the amount of speed/heading change. These action parameters are used to generate intermediate way-points, subsequently translated into a minimum snap trajectory, to be executed by a PD controller. This work presents a learning-based approach to train such reciprocal maneuvers. A Neuroevolution approach, which uses evolutionary algorithms to optimize the topology and weights of neural networks simultaneously, is used as the learning method--which operates over a set of sample approach scenarios to evaluate the fitness of each neural network candidate. Unlike most existing work (that minimize travel distance), the training process here has the capability of minimizing the required detection range and detection time. The specialized design of experiments and line search is used to identify the minimum detection range for each sample scenarios. This training process can also handle substantial challenges like uncertain UAV localization. For that, the relative pose of the other UAV, estimated by each UAV (at the point of detection), is considered to be uncertain. These capabilities have essential practical implications w.r.t. alleviating the dependency on sophisticated sensing and their reliability under various environments. Performing supervised learning based on optimization derived labels (as done in prior work) becomes computationally burdensome under these uncertainties. For an efficient training process, a classifier is used to discard actions (without simulating them) where the controller would fail. Also, a surrogate model is used to estimate the energy consumption and minimum distance between UAVs. The model obtained via neuroevolution is observed to generalize well to (i.e., successful collision avoidance over) unseen approach scenarios.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84049"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["mechanical engineering","robotics"],"dc:title":["Modeling Framework for Collision Avoidance Learning in Multirotor Unmanned Aerial Variables"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:30Z"}