Optimized Area Coverage in Disaster Response Utilizing Autonomous UAV Swarm Formations
Tech Hive Labs；Control Systems and Robotics Lab (CSRL)（控制系统与机器人实验室）；National Technical University of Athens（国家技术大学雅典）；Fédération ENAC ISAE-SUPAERO ONERA（ENAC ISAE-SUPAERO ONERA联合会）；AI Innovation Centre（人工智能创新中心）
This paper presents a UAV swarm system designed to assist first responders in disaster scenarios like wildfires. By distributing sensors across multiple agents, the system extends flight duration and enhances data availability, reducing the risk of mission failure due to collisions. To mitigate this risk further, we introduce an autonomous navigation framework that utilizes a local Euclidean Signed Distance Field (ESDF) map for obstacle avoidance while maintaining swarm formation with minimal path deviation. Additionally, we incorporate a Traveling Salesman Problem (TSP) variant to optimize area coverage, prioritizing Points of Interest (POIs) based on preassigned values derived from environmental behavior and critical infrastructure. The proposed system is validated through simulations with varying swarm sizes, demonstrating its ability to maximize coverage while ensuring collision avoidance between UAVs and obstacles.
Emergency happens in human-UAV cooperation, such as criminal activity tracking and urgent needs for ground assistance. Emergency response usually has high requirements on the motion control of the multi-UAV system, by maintaining both the team performance and team behaviors. However When a UAV swarm executes tasks in a real-world environment, because of real-world factors, such as system reliability and environmental disturbances, some robots in the swarm will behave abnormally, such as slow flocking speed, wrong heading direction, or poor spatial relations. In the meanwhile, incorrect trust b…
Simulation environments for Unmanned Aerial Vehicles (UAVs) can be very useful for prototyping user interfaces and training personnel that will operate UAVs in the real world. The realistic operation of such simulations will only enhance the value of such training. In this paper, we present the integration of a model-based waypoint navigation controller into the Reno Rescue Simulator for the purposes of providing a more realistic user interface in simulated environments. We also present potential uses for such simulations, even for real-world operation of UAVs.
Energy efficiency is of critical importance to trajectory planning for UAV swarms in obstacle avoidance. In this paper, we present $E^2Coop$, a new scheme designed to avoid collisions for UAV swarms by tightly coupling Artificial Potential Field (APF) with Particle Swarm Planning (PSO) based trajectory planning. In $E^2Coop$, swarm members perform trajectory planning cooperatively to avoid collisions in an energy-efficient manner. $E^2Coop$ exploits the advantages of the active contour model in image processing for trajectory planning. Each swarm member plans its trajectories on the contours o…
We propose a unified framework for coordinating Unmanned Aerial Vehicle (UAV) swarms operating under time-varying communication networks. Our framework builds on the concept of graphical games, which we argue provides a compelling paradigm to subsume the interaction structures found in networked UAV swarms thanks to the shared local neighborhood properties. We present a general-sum, factorizable payoff function for cooperative UAV swarms based on the aggregated local states and yield a Nash equilibrium for the stage games. Further, we propose a decomposition-based approach to solve stage-graph…
VNU University of Engineering and Technology（越南国立大学工程技术大学）；Fulbright University Vietnam（越南富布赖特大学）
This study proposes an approach for establishing an optimal multihop ad-hoc network using multiple unmanned aerial vehicles (UAVs) to provide emergency communication in disaster areas. The approach includes two stages, one uses particle swarm optimization (PSO) to find optimal positions to deploy UAVs, and the other uses a behavior-based controller to navigate the UAVs to their assigned positions without colliding with obstacles in an unknown environment. Several constraints related to the UAVs' sensing and communication ranges have been imposed to ensure the applicability of the proposed appr…
Multimodal Large Language Models-Enabled UAV Swarm: Towards Efficient and Intelligent Autonomous Aerial Systems
College of Informatics, Harbin Institute of Technology（信息学院，哈尔滨工业大学）；Key Laboratory of Forest and Grassland Fire Risk Prevention, Ministry of Emergency Management, China Fire and Rescue Institute（森林和草原火灾风险预防重点实验室，应急管理部，中国消防救援学院）；Southern University of Science and Technology（南方科技大学）
Talk Less, Fly Lighter: Autonomous Semantic Compression for UAV Swarm Communication via LLMs
Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology（工程科学系，创新工程学院，澳门科学理工学院）；State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences（复杂系统管理与控制国家重点实验室，自动化研究所，中国科学院）
This paper proposes a perception-shared and swarm trajectory global optimal (STGO) algorithm fused UAVs formation motion planning framework aided by an active sensing system. First, the point cloud received by each UAV is fit by the gaussian mixture model (GMM) and transmitted in the swarm. Resampling from the received GMM contributes to a global map, which is used as the foundation for consensus. Second, to improve flight safety, an active sensing system is designed to plan the observation angle of each UAV considering the unknown field, overlap of the field of view (FOV), velocity direction …
Technische Universität München（慕尼黑工业大学）；Tsinghua University（清华大学）；Avic Shenyang Aircraft Design And Research Institute（中国航空工业集团沈阳飞机设计研究所）
For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve full coverage of the area in the presence of obstacles. However, the endurance capability of UAV is limited, and the rescue time is urgent. Thus, even using multiple UAVs cannot achieve complete disaster area coverage in time. Therefore, in this paper we propose a multi-Agent Endurance-limited CPP (MAEl-CPP) problem based on a priori heatmap of the di…
Åbo Akademi University（奥布学术大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
With the growing popularity of Unmanned Aerial Vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation can not be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the…
University of Leeds（利兹大学）；University of Sheffield（谢菲尔德大学）
This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from e…
UAV swarms have triggered wide concern due to their potential application values in recent years. While there are studies proposed in terms of the architecture design for UAV swarms, two main challenges still exist: (1) Scalability, supporting a large scale of vehicles; (2) Versatility, integrating diversified missions. To this end, a multi-layered and distributed architecture for mission oriented miniature fixed-wing UAV swarms is presented in this paper. The proposed architecture is built on the concept of modularity. It divides the overall system to five layers: low-level control, high-leve…
Secure Multi-hop Telemetry Broadcasts for UAV Swarm Communication
Brandenburg University of Technology, Cottbus, Germany（勃兰登堡技术大学）；IHP - Leibniz Institute for High-Performance Microelectronics, Frankfurt (Oder), Germany（莱比锡高性能微电子研究所）
Czech Technical University in Prague（布拉格捷克理工大学）；Ricerca sul Sistema Energetico (RSE) S.p.A.（意大利能源系统研究公司（RSE））
This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of relative positions of UAVs,…
University of Padova（帕多瓦大学）；Aalborg University（奥尔堡大学）
Over the past few years, the use of swarms of Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to the price reduction and the increased capabilities of drones. The drones in the swarm need to cooperatively explore an unknown area, in order to identify and monitor interesting targets, while minimizing their movements. In this work, we propose a distributed Reinforcement Learning (RL) approach that scales to larger swarms without modifications. The proposed framework relies on the possibility for the UAVs to exchange some inform…
University of Maryland, College Park（马里兰大学帕克分校）；Institute for Systems Research（系统研究所）
We explore an end-to-end (including access and backhaul links) UAV-assisted wireless communication system, considering both uplink and downlink traffics, with the goal of supporting demand of the Ground Users (GUs) using the minimum number of UAVs. Moreover, in order to extend the operational (flight) time of UAVs, we exploit an energy-aware routing scheme. Our intention is to design and analyze the access and backhaul connectivity of a drone-assisted communication network for remote and crowded areas and disaster relief, while minimizing the resources required i.e., the number of UAVs.
Defending against Intrusion of Malicious UAVs with Networked UAV Defense Swarms
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg（安全、可靠与信任 interdisciplinary 中心（SnT），卢森堡大学，卢森堡）；Faculty of Science, Technology and Communication (FSTC), University of Luxembourg, Luxembourg（科学、技术与通信学院（FSTC），卢森堡大学，卢森堡）
An LLM-based Framework for Human-Swarm Teaming Cognition in Disaster Search and Rescue
School of Electronics and Information, Northwestern Polytechnical University（电子工程学院，西北工业大学）；Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory（脑科学与智能研究院，广阳湾实验室）
Virginia Tech（弗吉尼亚理工大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；Ericsson Research（爱立信研究院）；Princeton University（普林斯顿大学）；University of Oulu（奥卢大学）
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a l…
Bio-inspired visual relative localization for large swarms of UAVs
Czech Technical University in Prague（布拉格捷克技术大学）；Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克技术大学电气工程学院）；University of Zagreb（萨格勒布大学）；Faculty of Electrical Engineering and Computing, University of Zagreb（萨格勒布大学电气工程与计算机科学学院）；Laboratory for Robotics and Intelligent Control Systems (LARICS)（机器人与智能控制系统实验室（LARICS））
University of Technology Sydney（悉尼科技大学）；VNU University of Engineering and Technology（越南河内国家大学工程技术大学）
This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm includ…
Flying by Inference: Active Inference World Models for Adaptive UAV Swarms
Department of Engineering and Naval Architecture (DITEN), University of Genoa（工程与 naval 架构系（DITEN），热那亚大学）；Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Carlos III University of Madrid（智能系统实验室，系统工程与自动化系，马德里卡洛斯三世大学）
Communication-Free Collective Navigation for a Swarm of UAVs via LiDAR-Based Deep Reinforcement Learning
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology（机械工程系，釜山国立科学技术研究院）；Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology（机械工程系，韩国科学技术院）
Multiple robotic systems, working together, can provide important solutions to different real-world applications (e.g., disaster response), among which task allocation problems feature prominently. Very few existing decentralized multi-robotic task allocation (MRTA) methods simultaneously offer the following capabilities: consideration of task deadlines, consideration of robot range and task completion capacity limitations, and allowing asynchronous decision-making under dynamic task spaces. To provision these capabilities, this paper presents a computationally efficient algorithm that involve…
