{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/139860"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/139860","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Collective Motion in Spatially Heterogeneous Environments","abstract":"This dissertation investigates how spatial constraints influence collective motion through agent-based modeling and empirical analysis of animal behavior. We study how obstacles affect flocking, developed an agent-based model of bat flight from experimental data, and explored a model-free classification tool for swarming datasets. In the first study, we incorporated physical violation rules to an existing canonical flocking model to understand obstacle-induced perturbations to flocking behavior. The findings revealed that obstacles introduce non-monotonic phases of flocking. This non-monotonicity is tied to flock density, which is in turn mediated by noise and a characteristic length scale - the ratio of sensing radius to the obstacle radius. We found that particles need to sense in the order of the obstacle size to maintain global order. However, a high sensing radius can causes interlocked flocks to collide into virtual obstacles. Hence, while noise in the system generally frustrates global order, it is observed to facilitate flocking by breaking interlocked groups. Next, we extended the study to wildlife systems by analyzing the flight of gray bats. In this second study, we collected experimental data of bats flying in the absence and presence of environmental obstacles, and compared their flight under both conditions. Statistical and agent-based modeling of bat flight revealed that bats shift from spatial-memory driven channel-following behaviors to socially repulsive behavior in constrained spaces. Additionally, we found that obstacle avoidance dominates other interactions, suggesting that environmental feedback supersedes social cohesion under the stress of a novel environment. In the final study, we assessed swarming dynamics from 3D data projected into multiple 2D camera and pixel datasets to examine whether causal information can be retained in low-dimensional and noisy representations. Using midge swarming datasets, the analysis showed that EUGENE, the classification tool employed, performs better in constrained conditions such as cross-wind motion. This result implied that constrained dynamical systems are more sensitive to causal learning and similarity analyses. Overall, the studies in this dissertation reveal that spatial constraints, while limiting freedom of motion, accentuate local interactions and social structures. Counterintuitively, noise (or confusion) and reduced coordination may enhance collective motion in constrained environments.","abstract_html":"This dissertation investigates how spatial constraints influence collective motion through agent-based modeling and empirical analysis of animal behavior. We study how obstacles affect flocking, developed an agent-based model of bat flight from experimental data, and explored a model-free classification tool for swarming datasets. In the first study, we incorporated physical violation rules to an existing canonical flocking model to understand obstacle-induced perturbations to flocking behavior. The findings revealed that obstacles introduce non-monotonic phases of flocking. This non-monotonicity is tied to flock density, which is in turn mediated by noise and a characteristic length scale - the ratio of sensing radius to the obstacle radius. We found that particles need to sense in the order of the obstacle size to maintain global order. However, a high sensing radius can causes interlocked flocks to collide into virtual obstacles. Hence, while noise in the system generally frustrates global order, it is observed to facilitate flocking by breaking interlocked groups. Next, we extended the study to wildlife systems by analyzing the flight of gray bats. In this second study, we collected experimental data of bats flying in the absence and presence of environmental obstacles, and compared their flight under both conditions. Statistical and agent-based modeling of bat flight revealed that bats shift from spatial-memory driven channel-following behaviors to socially repulsive behavior in constrained spaces. Additionally, we found that obstacle avoidance dominates other interactions, suggesting that environmental feedback supersedes social cohesion under the stress of a novel environment. In the final study, we assessed swarming dynamics from 3D data projected into multiple 2D camera and pixel datasets to examine whether causal information can be retained in low-dimensional and noisy representations. Using midge swarming datasets, the analysis showed that EUGENE, the classification tool employed, performs better in constrained conditions such as cross-wind motion. This result implied that constrained dynamical systems are more sensitive to causal learning and similarity analyses. Overall, the studies in this dissertation reveal that spatial constraints, while limiting freedom of motion, accentuate local interactions and social structures. Counterintuitively, noise (or confusion) and reduced coordination may enhance collective motion in constrained environments.","abstract_has_math":false,"creators":["Aung, Eighdi"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Engineering Mechanics","degree_department":"Engineering Science and Mechanics","school":null,"contributors":[],"advisors":[],"committee_chairs":["Abaid, Nicole Teresa"],"committee_members":["Socha, John","Jantzen, Benjamin C.","Ross, Shane David"],"year":2025,"date_issued":"2025-12-09","date_published":"2025-12-09","updated_at":"2026-07-22T22:18:57Z","subjects":["agent-based modeling","causal similarity analysis","collective motion","flocking phases","geometry","model-based inference","model-free inference","sociality","spatial constraints"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44982"],"render_values":[{"text":"vt_gsexam:44982","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/139860","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Abaid, Nicole Teresa"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Socha, John","Jantzen, Benjamin C.","Ross, Shane David"]},{"key":"dc:contributor.department","label":"Department","values":["Engineering Science and Mechanics"]},{"key":"dc:creator","label":"Author","values":["Aung, Eighdi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-10T09:01:02Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-10T09:01:02Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-09"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Mechanics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["agent-based modeling","causal similarity analysis","collective motion","flocking phases","geometry","model-based inference","model-free inference","sociality","spatial constraints"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:44982"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/139860"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation investigates how spatial constraints influence collective motion through agent-based modeling and empirical analysis of animal behavior. We study how obstacles affect flocking, developed an agent-based model of bat flight from experimental data, and explored a model-free classification tool for swarming datasets. In the first study, we incorporated physical violation rules to an existing canonical flocking model to understand obstacle-induced perturbations to flocking behavior. The findings revealed that obstacles introduce non-monotonic phases of flocking. This non-monotonicity is tied to flock density, which is in turn mediated by noise and a characteristic length scale - the ratio of sensing radius to the obstacle radius. We found that particles need to sense in the order of the obstacle size to maintain global order. However, a high sensing radius can causes interlocked flocks to collide into virtual obstacles. Hence, while noise in the system generally frustrates global order, it is observed to facilitate flocking by breaking interlocked groups. Next, we extended the study to wildlife systems by analyzing the flight of gray bats. In this second study, we collected experimental data of bats flying in the absence and presence of environmental obstacles, and compared their flight under both conditions. Statistical and agent-based modeling of bat flight revealed that bats shift from spatial-memory driven channel-following behaviors to socially repulsive behavior in constrained spaces. Additionally, we found that obstacle avoidance dominates other interactions, suggesting that environmental feedback supersedes social cohesion under the stress of a novel environment. In the final study, we assessed swarming dynamics from 3D data projected into multiple 2D camera and pixel datasets to examine whether causal information can be retained in low-dimensional and noisy representations. Using midge swarming datasets, the analysis showed that EUGENE, the classification tool employed, performs better in constrained conditions such as cross-wind motion. This result implied that constrained dynamical systems are more sensitive to causal learning and similarity analyses. Overall, the studies in this dissertation reveal that spatial constraints, while limiting freedom of motion, accentuate local interactions and social structures. Counterintuitively, noise (or confusion) and reduced coordination may enhance collective motion in constrained environments."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["This dissertation explores how physical boundaries and obstacles influence the way groups move and work together. We simulated flocking motion with obstacles, built a mathematical model validated with an experimental dataset we collected, and examined techniques to understand similarities in group motion without the use of a model. In the first part, we simulated movement similar to how birds flock but added obstacles to ask: How much does a group member need to sense to maintain flocking? We found that each member needs to sense up to the size of an obstacle to flock in large groups. However, sensing too much can also frustrate flocking due to an interlocked state where everyone responds to an obstacle even if only some part of the group encounters it. In such cases, confusion in the group helps it break from this interlocked state and achieve better flocking. In the second part, we collected real-world data from gray bats, and compared the data of their flight in open space versus when we introduced obstacles in their flight paths. We learned that, in the absence of obstacles, bats followed familiar routes from spatial memory. However, when obstacles are introduced, the bats responded socially to their nearest neighbors by flying further from them. They also showed obstacle avoidance behaviors. This indicated that when facing new environments, animals prioritize social and environmental cues over memory. Finally, the third study examined whether complex group motion can still be differentiated using limited or low-quality data, such as 2D video recordings. We found that even limited data can be used to differentiate group motion. Even more so, we learned that datasets where motion is more restrictive is more identifiable. Together, these studies reveal that restrictions in the space, while frustrating order in the groups, can actually make social interactions more visible. Surprisingly, a little confusion or noise in such systems can improve coordination, and sometimes animals may even choose to separate from the group to possibly ease their navigation."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Collective Motion in Spatially Heterogeneous Environments"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Abaid, Nicole Teresa"],"dc:contributor.committeemember":["Socha, John","Jantzen, Benjamin C.","Ross, Shane David"],"dc:contributor.department":["Engineering Science and Mechanics"],"dc:creator":["Aung, Eighdi"],"dc:date.accessioned":["2025-12-10T09:01:02Z"],"dc:date.available":["2025-12-10T09:01:02Z"],"dc:date.issued":["2025-12-09"],"dc:description.abstract":["This dissertation investigates how spatial constraints influence collective motion through agent-based modeling and empirical analysis of animal behavior. We study how obstacles affect flocking, developed an agent-based model of bat flight from experimental data, and explored a model-free classification tool for swarming datasets. In the first study, we incorporated physical violation rules to an existing canonical flocking model to understand obstacle-induced perturbations to flocking behavior. The findings revealed that obstacles introduce non-monotonic phases of flocking. This non-monotonicity is tied to flock density, which is in turn mediated by noise and a characteristic length scale - the ratio of sensing radius to the obstacle radius. We found that particles need to sense in the order of the obstacle size to maintain global order. However, a high sensing radius can causes interlocked flocks to collide into virtual obstacles. Hence, while noise in the system generally frustrates global order, it is observed to facilitate flocking by breaking interlocked groups. Next, we extended the study to wildlife systems by analyzing the flight of gray bats. In this second study, we collected experimental data of bats flying in the absence and presence of environmental obstacles, and compared their flight under both conditions. Statistical and agent-based modeling of bat flight revealed that bats shift from spatial-memory driven channel-following behaviors to socially repulsive behavior in constrained spaces. Additionally, we found that obstacle avoidance dominates other interactions, suggesting that environmental feedback supersedes social cohesion under the stress of a novel environment. In the final study, we assessed swarming dynamics from 3D data projected into multiple 2D camera and pixel datasets to examine whether causal information can be retained in low-dimensional and noisy representations. Using midge swarming datasets, the analysis showed that EUGENE, the classification tool employed, performs better in constrained conditions such as cross-wind motion. This result implied that constrained dynamical systems are more sensitive to causal learning and similarity analyses. Overall, the studies in this dissertation reveal that spatial constraints, while limiting freedom of motion, accentuate local interactions and social structures. Counterintuitively, noise (or confusion) and reduced coordination may enhance collective motion in constrained environments."],"dc:description.abstractgeneral":["This dissertation explores how physical boundaries and obstacles influence the way groups move and work together. We simulated flocking motion with obstacles, built a mathematical model validated with an experimental dataset we collected, and examined techniques to understand similarities in group motion without the use of a model. In the first part, we simulated movement similar to how birds flock but added obstacles to ask: How much does a group member need to sense to maintain flocking? We found that each member needs to sense up to the size of an obstacle to flock in large groups. However, sensing too much can also frustrate flocking due to an interlocked state where everyone responds to an obstacle even if only some part of the group encounters it. In such cases, confusion in the group helps it break from this interlocked state and achieve better flocking. In the second part, we collected real-world data from gray bats, and compared the data of their flight in open space versus when we introduced obstacles in their flight paths. We learned that, in the absence of obstacles, bats followed familiar routes from spatial memory. However, when obstacles are introduced, the bats responded socially to their nearest neighbors by flying further from them. They also showed obstacle avoidance behaviors. This indicated that when facing new environments, animals prioritize social and environmental cues over memory. Finally, the third study examined whether complex group motion can still be differentiated using limited or low-quality data, such as 2D video recordings. We found that even limited data can be used to differentiate group motion. Even more so, we learned that datasets where motion is more restrictive is more identifiable. Together, these studies reveal that restrictions in the space, while frustrating order in the groups, can actually make social interactions more visible. Surprisingly, a little confusion or noise in such systems can improve coordination, and sometimes animals may even choose to separate from the group to possibly ease their navigation."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:44982"],"dc:identifier.uri":["https://hdl.handle.net/10919/139860"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["agent-based modeling","causal similarity analysis","collective motion","flocking phases","geometry","model-based inference","model-free inference","sociality","spatial constraints"],"dc:title":["Collective Motion in Spatially Heterogeneous Environments"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Engineering Mechanics"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:57Z"}