{"id":{"repo_id":"exeter","oai_identifier":"oai:figshare.com:article/31916157"},"canonical_url":"https://search.dev.ndltd.org/etd/exeter/oai:figshare.com:article/31916157","repository":{"repo_id":"exeter","name":"University of Exeter","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"Collaborative Autonomy of Multi-Agent Aerial Systems for Future Disaster Response","abstract":"Effective disaster response is increasingly challenged by the growing frequency and severity of natural and human-induced disasters worldwide. In such scenarios, response efficiency is primarily constrained by unreliable and severely delayed communications, inaccessible and hazardous terrain, limited resources and real-time situational awareness, which collectively hinder timely decision-making and coordinated operations. To address these challenges, this thesis investigates intelligent control, cooperative navigation, resource allocation, and agentic decision-making for multi-agent aerial systems. Chapter 1 provides the research background and motivation for developing collaborative autonomy in multi-agent aerial systems for disaster response applications. Chapter 2 reviews the state of the art in several areas, covering formation control, swarm path planning, resource allocation, and LLM-based methods. Chapter 3 develops distributed time-varying formation control protocols under dynamic communication topologies with multiple delays and computes the maximum tolerated delay for reliable coordination. Building on this foundation, Chapter 4 proposes a distributed 3D cooperative path-planning framework for large-scale UAV swarms in cluttered environments. A hybrid neighbor-forecasting strategy using a lightweight Transformer combined with constant-velocity prediction is developed, along with a learned local descriptor for directional crowding quantification and entropy-based trajectory evaluation to maintain diversity and alleviate congestion. Chapter 5 introduces a fully decentralized UAV-assisted mobile edge computing framework consisting of a K-Grouped Soft Actor-Critic with Trajectory Planning (KGSAC-TP) algorithm for UAV and a User Switching Mechanism (USM) for heterogeneous users. Furthermore, Chapter 6 designed an Agentic Retrieval-Augmented Generation framework that enables knowledge-augmented perception, situational awareness, and goal-oriented multi-robot coordination in complex disaster scenarios to generate a comprehensive report for human experts to support disaster response decision-making. Finally, Chapter 7 concludes the thesis by summarizing the key findings and discussing potential directions for future research. This thesis provides novel strategies combining control theory, artificial intelligence (AI)-driven policy, multi-agent deep reinforcement learning, and agentic AI for collaborative multi-agent aerial systems in disaster response.<p></p>","abstract_html":"Effective disaster response is increasingly challenged by the growing frequency and severity of natural and human-induced disasters worldwide. In such scenarios, response efficiency is primarily constrained by unreliable and severely delayed communications, inaccessible and hazardous terrain, limited resources and real-time situational awareness, which collectively hinder timely decision-making and coordinated operations. To address these challenges, this thesis investigates intelligent control, cooperative navigation, resource allocation, and agentic decision-making for multi-agent aerial systems. Chapter 1 provides the research background and motivation for developing collaborative autonomy in multi-agent aerial systems for disaster response applications. Chapter 2 reviews the state of the art in several areas, covering formation control, swarm path planning, resource allocation, and LLM-based methods. Chapter 3 develops distributed time-varying formation control protocols under dynamic communication topologies with multiple delays and computes the maximum tolerated delay for reliable coordination. Building on this foundation, Chapter 4 proposes a distributed 3D cooperative path-planning framework for large-scale UAV swarms in cluttered environments. A hybrid neighbor-forecasting strategy using a lightweight Transformer combined with constant-velocity prediction is developed, along with a learned local descriptor for directional crowding quantification and entropy-based trajectory evaluation to maintain diversity and alleviate congestion. Chapter 5 introduces a fully decentralized UAV-assisted mobile edge computing framework consisting of a K-Grouped Soft Actor-Critic with Trajectory Planning (KGSAC-TP) algorithm for UAV and a User Switching Mechanism (USM) for heterogeneous users. Furthermore, Chapter 6 designed an Agentic Retrieval-Augmented Generation framework that enables knowledge-augmented perception, situational awareness, and goal-oriented multi-robot coordination in complex disaster scenarios to generate a comprehensive report for human experts to support disaster response decision-making. Finally, Chapter 7 concludes the thesis by summarizing the key findings and discussing potential directions for future research. This thesis provides novel strategies combining control theory, artificial intelligence (AI)-driven policy, multi-agent deep reinforcement learning, and agentic AI for collaborative multi-agent aerial systems in disaster response.&lt;p&gt;&lt;/p&gt;","abstract_has_math":false,"creators":["Jia Wu (21042131)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-04-07T00:00:00Z","date_published":"2026-04-07T00:00:00Z","updated_at":"2026-07-27T19:33:36Z","subjects":["Multi-agent aerial systems","AI-driven policy","Deep reinforcement learning"],"languages":[],"rights":["All rights reserved","Open Access after 2027-10-07"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10779/exe.31916157.v1"],"render_values":[{"text":"10779/exe.31916157.v1","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Jia Wu (21042131)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-04-07T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/Collaborative_Autonomy_of_Multi-Agent_Aerial_Systems_for_Future_Disaster_Response/31916157"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multi-agent aerial systems","AI-driven policy","Deep reinforcement learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["All rights reserved","Open Access after 2027-10-07"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10779/exe.31916157.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Effective disaster response is increasingly challenged by the growing frequency and severity of natural and human-induced disasters worldwide. In such scenarios, response efficiency is primarily constrained by unreliable and severely delayed communications, inaccessible and hazardous terrain, limited resources and real-time situational awareness, which collectively hinder timely decision-making and coordinated operations. To address these challenges, this thesis investigates intelligent control, cooperative navigation, resource allocation, and agentic decision-making for multi-agent aerial systems. Chapter 1 provides the research background and motivation for developing collaborative autonomy in multi-agent aerial systems for disaster response applications. Chapter 2 reviews the state of the art in several areas, covering formation control, swarm path planning, resource allocation, and LLM-based methods. Chapter 3 develops distributed time-varying formation control protocols under dynamic communication topologies with multiple delays and computes the maximum tolerated delay for reliable coordination. Building on this foundation, Chapter 4 proposes a distributed 3D cooperative path-planning framework for large-scale UAV swarms in cluttered environments. A hybrid neighbor-forecasting strategy using a lightweight Transformer combined with constant-velocity prediction is developed, along with a learned local descriptor for directional crowding quantification and entropy-based trajectory evaluation to maintain diversity and alleviate congestion. Chapter 5 introduces a fully decentralized UAV-assisted mobile edge computing framework consisting of a K-Grouped Soft Actor-Critic with Trajectory Planning (KGSAC-TP) algorithm for UAV and a User Switching Mechanism (USM) for heterogeneous users. Furthermore, Chapter 6 designed an Agentic Retrieval-Augmented Generation framework that enables knowledge-augmented perception, situational awareness, and goal-oriented multi-robot coordination in complex disaster scenarios to generate a comprehensive report for human experts to support disaster response decision-making. Finally, Chapter 7 concludes the thesis by summarizing the key findings and discussing potential directions for future research. This thesis provides novel strategies combining control theory, artificial intelligence (AI)-driven policy, multi-agent deep reinforcement learning, and agentic AI for collaborative multi-agent aerial systems in disaster response.<p></p>"]},{"key":"dc:title","label":"Title","values":["Collaborative Autonomy of Multi-Agent Aerial Systems for Future Disaster Response"]}]}],"canonical_facts":{"dc:creator":["Jia Wu (21042131)"],"dc:date":["2026-04-07T00:00:00Z"],"dc:description":["Effective disaster response is increasingly challenged by the growing frequency and severity of natural and human-induced disasters worldwide. In such scenarios, response efficiency is primarily constrained by unreliable and severely delayed communications, inaccessible and hazardous terrain, limited resources and real-time situational awareness, which collectively hinder timely decision-making and coordinated operations. To address these challenges, this thesis investigates intelligent control, cooperative navigation, resource allocation, and agentic decision-making for multi-agent aerial systems. Chapter 1 provides the research background and motivation for developing collaborative autonomy in multi-agent aerial systems for disaster response applications. Chapter 2 reviews the state of the art in several areas, covering formation control, swarm path planning, resource allocation, and LLM-based methods. Chapter 3 develops distributed time-varying formation control protocols under dynamic communication topologies with multiple delays and computes the maximum tolerated delay for reliable coordination. Building on this foundation, Chapter 4 proposes a distributed 3D cooperative path-planning framework for large-scale UAV swarms in cluttered environments. A hybrid neighbor-forecasting strategy using a lightweight Transformer combined with constant-velocity prediction is developed, along with a learned local descriptor for directional crowding quantification and entropy-based trajectory evaluation to maintain diversity and alleviate congestion. Chapter 5 introduces a fully decentralized UAV-assisted mobile edge computing framework consisting of a K-Grouped Soft Actor-Critic with Trajectory Planning (KGSAC-TP) algorithm for UAV and a User Switching Mechanism (USM) for heterogeneous users. Furthermore, Chapter 6 designed an Agentic Retrieval-Augmented Generation framework that enables knowledge-augmented perception, situational awareness, and goal-oriented multi-robot coordination in complex disaster scenarios to generate a comprehensive report for human experts to support disaster response decision-making. Finally, Chapter 7 concludes the thesis by summarizing the key findings and discussing potential directions for future research. This thesis provides novel strategies combining control theory, artificial intelligence (AI)-driven policy, multi-agent deep reinforcement learning, and agentic AI for collaborative multi-agent aerial systems in disaster response.<p></p>"],"dc:identifier":["10779/exe.31916157.v1"],"dc:relation":["https://figshare.com/articles/thesis/Collaborative_Autonomy_of_Multi-Agent_Aerial_Systems_for_Future_Disaster_Response/31916157"],"dc:rights":["All rights reserved","Open Access after 2027-10-07"],"dc:subject":["Multi-agent aerial systems","AI-driven policy","Deep reinforcement learning"],"dc:title":["Collaborative Autonomy of Multi-Agent Aerial Systems for Future Disaster Response"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:33:36Z"}