Existing studies on formation control for unmanned aerial vehicles (UAV) have not considered encircling targets where an optimum coverage of the target is required at all times. Such coverage plays a critical role in many real-world applications such as tracking hostile UAVs. This paper proposes a new path planning approach called the Flux Guided (FG) method, which generates collision-free trajectories for multiple UAVs while maximising the coverage of target(s). Our method enables UAVs to track directly toward a target whilst maintaining maximum coverage. Furthermore, multiple scattered targets can be tracked by scaling the formation during flight. FG is highly scalable since it only requires communication between sub-set of UAVs on the open boundary of the formation's surface. Experimental results further validate that FG generates UAV trajectories $1.5 \times$ shorter than previous work and that trajectory planning for 9 leader/follower UAVs to surround a target in two different scenarios only requires 0.52 seconds and 0.88 seconds, respectively. The resulting trajectories are suitable for robotic controls after time-optimal parameterisation; we demonstrate this using a 3d dynamic particle system that tracks the desired trajectories using a PID controller.
Tongji University（同济大学）；the University of Hong Kong（香港大学）
Vision-based object tracking has boosted extensive autonomous applications for unmanned aerial vehicles (UAVs). However, the dynamic changes in flight maneuver and viewpoint encountered in UAV tracking pose significant difficulties, e.g. , aspect ratio change, and scale variation. The conventional cross-correlation operation, while commonly used, has limitations in effectively capturing perceptual similarity and incorporates extraneous background information. To mitigate these limitations, this work presents a novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. The prop…
Unmanned aerial vehicle (UAV) path planning enables UAVs to avoid obstacles and reach the target efficiently. To generate high-quality paths without obstacle collision for UAVs, this paper proposes a novel autonomous path planning algorithm based on a tangent intersection and target guidance strategy (APPATT). Guided by a target, the elliptic tangent graph method is used to generate two sub-paths, one of which is selected based on heuristic rules when confronting an obstacle. The UAV flies along the selected sub-path and repeatedly adjusts its flight path to avoid obstacles through this way un…
Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection
Department of Robotics, School of Advanced Manufacturing and Robotics（机器人学院，先进制造与机器人学院）；State Key Laboratory of Turbulence and Complex Systems（湍流与复杂系统国家重点实验室）；Peking University（北京大学）；Great Bay University（大湾大学）
SkyRover: A Modular Simulator for Cross-Domain Pathfinding
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Mathematical Sciences, East China Normal University（华东师范大学数学科学学院）
Autonomous exploration is one of the important parts to achieve the autonomous operation of Unmanned Aerial Vehicles (UAVs). To improve the efficiency of the exploration process, a fast and autonomous exploration planner (FAEP) is proposed in this paper. We firstly design a novel frontiers exploration sequence generation method to obtain a more reasonable exploration path, which considers not only the flight-level but frontier-level factors into TSP. According to the exploration sequence and the distribution of frontiers, a two-stage heading planning strategy is proposed to cover more frontier…
Autonomous UAV-Quadruped Docking in Complex Terrains via Active Posture Alignment and Constraint-Aware Control
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Frontiers Science Center for Intelligent Autonomous Systems, Shanghai（上海智能自主系统前沿科学中心）；School of Logistics Engineering, Shanghai Maritime University（上海 Maritime University物流工程学院）；State Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai（上海自主智能无人系统国家重点实验室）
Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations
University of Zagreb（萨格勒布大学）；Faculty of Electrical Engineering and Computing（电气工程与计算机学院）；LARICS (Laboratory for Robotics and Intelligent Control Systems)（拉里克机器人与智能控制系统实验室）
Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所认知与决策智能复杂系统重点实验室）
UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning
Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；National University of Singapore（新加坡国立大学）；MMLab, CUHK（香港中文大学MMLab）；Hangzhou International Innovation Institute of Beihang University（北京航空航天大学杭州国际创新研究院）
HetSwarm: Cooperative Navigation of Heterogeneous Swarm in Dynamic and Dense Environments through Impedance-based Guidance
Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology（斯克尔科夫科学与技术研究所智能空间机器人实验室，数字工程中心）；Multi-Robot Autonomy Laboratory, Department of Data Science and Engineering, IISER Bhopal（IISER博帕尔数据科学与工程系多机器人自主性实验室）
