This paper addresses the problem of autonomous task allocation by a swarm of autonomous, interactive drones in large-scale, dynamic spatio-temporal environments. When each drone independently determines navigation, sensing, and recharging options to choose from such that system-wide sensing requirements are met, the collective decision-making becomes an NP-hard decentralized combinatorial optimization problem. Existing solutions face significant limitations: distributed optimization methods such as collective learning often lack long-term adaptability, while centralized deep reinforcement learning (DRL) suffers from high computational complexity, scalability and privacy concerns. To overcome these challenges, we propose a novel hybrid optimization approach that combines long-term DRL with short-term collective learning. In this approach, each drone uses DRL methods to proactively determine high-level strategies, such as flight direction and recharging behavior, while leveraging collective learning to coordinate short-term sensing and navigation tasks with other drones in a decentralized manner. Extensive experiments using datasets derived from realistic urban mobility demonstrate that the proposed solution outperforms standalone state-of-the-art collective learning and DRL approaches by $27.83\%$ and $23.17\%$ respectively. Our findings highlight the complementary strengths of short-term and long-term decision-making, enabling energy-efficient, accurate, and sustainable traffic monitoring through swarms of drones.
Stanford University（斯坦福大学）；University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Universidad de Zaragoza（萨拉戈萨大学）
To be successful in multi-player drone racing, a player must not only follow the race track in an optimal way, but also compete with other drones through strategic blocking, faking, and opportunistic passing while avoiding collisions. Since unveiling one's own strategy to the adversaries is not desirable, this requires each player to independently predict the other players' future actions. Nash equilibria are a powerful tool to model this and similar multi-agent coordination problems in which the absence of communication impedes full coordination between the agents. In this paper, we propose a…
AirSwarm: Enabling Cost-Effective Multi-UAV Research with COTS drones
Centre for Advanced Robotics Technology Innovation (CARTIN)（高级机器人技术创新中心（CARTIN））；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Decentralized traffic management of autonomous drones
Eötvös University of Budapest（布达佩斯厄特沃什大学）；MTA-ELTE Statistical and Biological Physics Research Group（匈牙利科学院-厄特沃什大学统计与生物物理研究组）；CollMot Robotics LLC（CollMot机器人有限责任公司）
Enhancing Drone Light Shows Performances: Optimal Allocation and Trajectories for Swarm Drone Formations
Aerospace Engineering Department, Faculty of Aeronautics and Astronautics, Kocaeli University（航空航天工程系，航空航天学院，科伊亚利大学）；Interdisciplinary Research Centre for Aviation and Space Exploration (IRC-ASE), KFUPM（航空与航天探索交叉研究中心（IRC-ASE），国王费萨尔大学）
Python Library for simulating unManNed vehiclEs(PLANE) is an open source software module, written in Python, that focuses on Unmanned Aerial Vehicles (UAVs), on their movements and on the mechanics of flight, thus devoting particular attention to the equations that describe drones' movement. In the context of the Internet of Drones (IoD), the module can be widely used for the study of the mutual control of position/coordination in scenarios in which drones may find obstacles, as it happens in densely populated urban scenarios. Emphasis is put on ease of use, performance evaluation, documentati…
Suleyman Demirel University（苏莱曼·德米雷尔大学）；UNIST（蔚山科学技术院）；Korea University（高丽大学）；Amazon（亚马逊）；University of South Florida（南佛罗里达大学）
Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP). Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination -- a truck and a drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to mak…
Stanford University（斯坦福大学）；Technion - Israel Institute of Technology（以色列理工学院）
We address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coordination can yield significant savings in total vehicle distance traveled, i.e., truck travel distance and drone flight distance, compared to operating drones and trucks independently. But it comes at the potentially prohibitive computational cost of deciding which trucks and drones should coordinate and when and where it is most beneficial to do so. We tackle…
Real-Time Sense and Detect of Drones Using Deep Learning and Airborne LiDAR
Drone Systems Lab, School of Computing, Engineering, and Physical Sciences, University of the West of Scotland（无人机系统实验室，计算、工程与物理科学学院，西方苏格兰大学）；Centre for Robotics & Intelligent Systems, Department of Electronic & Computer Engineering, University of Limerick（机器人与智能系统中心，电子与计算机工程系，利默里克大学）；Hamburg Port Authority（汉堡港口管理局）
The weight, space, and power limitations of small aerial vehicles often prevent the application of modern control techniques without significant model simplifications. Moreover, high-speed agile behavior, such as that exhibited in drone racing, make these simplified models too unreliable for safety-critical control. In this work, we introduce the concept of time-varying backup controllers (TBCs): user-specified maneuvers combined with backup controllers that generate reference trajectories which guarantee the safety of nonlinear systems. TBCs reduce conservatism when compared to traditional ba…
Georgia Institute of Technology（佐治亚理工学院）；The University of Texas at Austin（得克萨斯大学奥斯汀分校）
Unmanned aerial vehicles or drones are becoming increasingly popular due to their low cost and high mobility. In this paper we address the routing and coordination of a drone-truck pairing where the drone travels to multiple locations to perform specified observation tasks and rendezvous periodically with the truck to swap its batteries. We refer to this as the Nested-Vehicle Routing Problem (Nested-VRP) and develop a Mixed Integer Quadratically Constrained Programming (MIQCP) formulation with critical operational constraints, including drone battery capacity and synchronization of both vehicl…
A highly maneuverable flying squirrel drone with agility-improving foldable wings
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH)（融合IT工程系，浦项科学技术大学）；Defence AI Center, Agency for Defense Development(ADD)（国防人工智能中心，国防开发机构）
Coordinated Autonomous Drones for Human-Centered Fire Evacuation in Partially Observable Urban Environments
Mechanical Engineering University of California, Berkeley（机械工程 加州大学伯克利分校）；Computer Sciences University of California, Berkeley（计算机科学 加州大学伯克利分校）；Computer Engineering Johns Hopkins University（计算机工程 约翰霍普金斯大学）
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
In this paper, we describe a robust multi-drone planning framework for high-speed trajectories in large scenes. It uses a free-space-oriented map to free the optimization from cumbersome environment data. A capsule-like safety constraint is designed to avoid reciprocal collisions when vehicles deviate from their nominal flight progress under disturbance. We further show the minimum-singularity differential flatness of our drone dynamics with nonlinear drag effects involved. Leveraging the flatness map, trajectory optimization is efficiently conducted on the flat outputs while still subject to …
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
Dalle Molle Institute for Artificial Intelligence (IDSIA) - SUPSI/USI（达勒莫尔人工智能研究所（IDSIA）- 瑞士南部应用科学与艺术大学/瑞士意大利语区大学）；armasuisse Science and Technology（瑞士联邦国防采购局科学与技术）
An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones
School of Management Science and Real Estate, Chongqing University（重庆大学管理科学与房地产学院）；School of Management, Guizhou University（贵州大学管理学院）；Chongqing Changan Minsheng APLL Logistics Co., Ltd.（重庆长安民生APLL物流有限公司）；State Grid Chongqing Electric Power Company Material Branch（国网重庆市电力公司物资分公司）
CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios
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，澳门科学技术大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University（应用信息与应用数学博士学院，奥布达大学）；School of Artificial Intelligence, Anhui University（人工智能学院，安徽大学）；State Key Laboratory for Management and Control of Complex Systems（复杂系统管理与控制国家重点实验室）
Task Coordination and Trajectory Optimization for Multi-Aerial Systems via Signal Temporal Logic: A Wind Turbine Inspection Study
Czech Technical University in Prague（布拉格捷克技术大学）；University of Seville（塞维利亚大学）；University of Sannio in Benevento（贝内文托萨尼奥大学）；ENEA（意大利国家新技术、能源与经济可持续发展局）；University of Zagreb（萨格勒布大学）
