Adaptive path planning for efficient object search by UAVs in agricultural fields
Agricultural Biosystems Engineering, Department of Plant Sciences, Wageningen University and Research（农业生物系统工程系，植物科学系，瓦赫宁根大学与研究中心）；Laboratory of Geo-information Science and Remote Sensing, Department of Environmental Sciences, Wageningen University and Research（地理信息科学与遥感实验室，环境科学系，瓦赫宁根大学与研究中心）
Rick van Essen, Eldert van Henten, Lammert Kooistra, Gert Kootstra
This paper presents an adaptive path planner for object search in agricultural fields using UAVs. The path planner uses a high-altitude coverage flight path and plans additional low-altitude inspections when the detection network is uncertain. The path planner was evaluated in an offline simulation environment containing real-world images. We trained a YOLOv8 detection network to detect artificial plants placed in grass fields to showcase the potential of our path planner. We evaluated the effect of different detection certainty measures, optimized the path planning parameters, investigated the effects of localization errors, and different numbers of objects in the field. The YOLOv8 detection confidence worked best to differentiate between true and false positive detections and was therefore used in the adaptive planner. The optimal parameters of the path planner depended on the distribution of objects in the field. When the objects were uniformly distributed, more low-altitude inspections were needed compared to a non-uniform distribution of objects, resulting in a longer path length. The adaptive planner proved to be robust against localization uncertainty. When increasing the number of objects, the flight path length increased, especially when the objects were uniformly distributed. When the objects were non-uniformly distributed, the adaptive path planner yielded a shorter path than a low-altitude coverage path, even with a high number of objects. Overall, the presented adaptive path planner allowed finding non-uniformly distributed objects in a field faster than a coverage path planner and resulted in a compatible detection accuracy. The path planner is made available at https://github.com/wur-abe/uav_adaptive_planner.
Route Fragmentation Based on Resource-centric Prioritisation for Efficient Multi-Robot Path Planning in Agricultural Environments
Lincoln Centre for Autonomous Systems, University of Lincoln, UK（林肯自主系统中心，林肯大学，英国）；Lincoln Institute for Agri-food Technology, University of Lincoln, UK（林肯农业食品技术研究所，林肯大学，英国）
Efficient and Safe Trajectory Planning for Autonomous Agricultural Vehicle Headland Turning in Cluttered Orchard Environments
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学ZJU-杭州全球科技创新中心）；University of California, Davis（加州大学戴维斯分校）；College Of Biosystems Engineering And Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）
Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague（控制系，电气工程系，布拉格捷克技术大学）；Department of Computer, Federal University of São Carlos（计算机系，萨o卡洛斯联邦大学）
Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；Department of Ocean Science and Engineering, Southern University of Science and Technology（南方科技大学海洋科学与工程系）；The State Key Laboratory of Turbulence and Complex Systems, Intelligent Biomimetic Design Lab, College of Engineering, Peking University（北京大学工学院湍流与复杂系统国家重点实验室智能仿生设计实验室）；National Engineering Research Center for Software Engineering, Peking University（北京大学软件工程国家工程研究中心）；Peng Cheng Laboratory（鹏城实验室）
A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction
Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig（布伦瑞克工业大学测量与摄影测量研究所）；University of Tehran（德黑兰大学）；Intelligent Robotics Research Division, ETRI（韩国电子通信研究院智能机器人研究部）；New York University（纽约大学）；University of Edinburgh（爱丁堡大学）
A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks
Centre for Wireless Communications (CWC), University of Oulu（奥卢大学无线通信中心（CWC））
许多新兴的物联网（IoT）应用依赖于传感器节点收集的信息，其中信息的新鲜度是一个重要标准。\textit{Age of Information}（AoI）是量化信息及时性的度量，即，接收到的信息或状态更新的新鲜度。这项工作考虑了物联网网络中部署的传感器的设置，其中多个无人机（UAV）作为传感器和基站之间的移动中继节点。我们制定了一个优化问题，共同规划无人机的轨迹，同时最小化收到的消息的AoI。这确保了在基站处接收的信息尽可能新鲜。使用深度强化学习（DRL）算法有效地解决了复杂的优化问题。特别是，我们提出了一个深度Q网络，它作为一个函数近似估计的状态-动作价值函数。所提出的方案是快速收敛，并在一个较低的AoI比随机游走计划的结果。我们提出的算法减少了约$25\%$的平均年龄，并需要下降到$50\%$更少的能量相比，基线计划。
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
TUM School of Engineering and Design, Technical University of Munich（慕尼黑技术大学工程与设计学院）；Dept. of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）
Multi-goal Rapidly Exploring Random Tree with Safety and Dynamic Constraints for UAV Cooperative Path Planning
Vietnam National University, Hanoi（越南河内国家大学）；Thai Nguyen University of Technology, Thai Nguyen University（太原大学太原工业学院）；University of Transport and Communications（交通通信大学）；Fulbright University Vietnam（越南富布赖特大学）
Joint UAV-UGV Positioning and Trajectory Planning via Meta A3C for Reliable Emergency Communications
Department of Computer Science, Texas A&M University-Corpus Christi（德克萨斯农工大学科珀斯克里斯蒂分校计算机科学系）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
Department of Cybernetics, Czech Technical University in Prague（捷克技术大学布拉格分校自动化系）；Mechanical and Aerospace Engineering, Old Dominion University（旧 Dominion 大学机械与航空航天工程系）；Mechanical Engineering Faculty, Sahand University of Technology（沙赫德大学机械工程系）；Aksaray University（阿克萨雷大学）
Connectivity-Aware UAV Path Planning with Aerial Coverage Maps
Department of ECE, Hong Kong University of Science and Technology, Hong Kong（香港科技大学电子工程系）；Department of EIE, The Hong Kong Polytechnic University, Hong Kong（香港理工大学电子工程系）
Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar
School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机科学学院）；TopXGun Robotics（TopXGun机器人）；CARDC；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子信息工程学院）
