Integrated Multi-Drone Task Allocation, Sequencing, and Optimal Trajectory Generation in Obstacle-Rich 3D Environments
Aerospace Engineering Department, Faculty of Aeronautics and Astronautics, Kocaeli University（科贾埃利大学航空与航天学院航空航天工程系）；Interdisciplinary Research Center for Aviation and Space Exploration, KFUPM（法赫德国王石油矿产大学航空与空间探索跨学科研究中心）；Mechanical Engineering Department, Faculty of Aeronautics and Astronautics, Kocaeli University（科贾埃利大学航空与航天学院机械工程系）
Yunes Alqudsi, Murat Makaraci
备注：
Resubmission following accepted appeal (MOD-78958). Resubmitting to cs.RO with cross-lists cs.MA and cs.AI as advised by arXiv Support
Coordinating teams of aerial robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which robot serves which goals and in what order -- with continuous-time trajectory synthesis that enforces collision avoidance and dynamic feasibility. This paper introduces IMD-TAPP (Integrated Multi-Drone Task Allocation and Path Planning), an end-to-end framework that jointly addresses multi-goal allocation, tour sequencing, and safe trajectory generation for quadrotor teams operating in obstacle-rich spaces. IMD--TAPP first discretizes the workspace into a 3D navigation graph and computes obstacle-aware robot-to-goal and goal-to-goal travel costs via graph-search-based pathfinding. These costs are then embedded within an Injected Particle Swarm Optimization (IPSO) scheme, guided by multiple linear assignment, to efficiently explore coupled assignment/ordering alternatives and to minimize mission makespan. Finally, the resulting waypoint tours are transformed into time-parameterized minimum-snap trajectories through a generation-and-optimization routine equipped with iterative validation of obstacle clearance and inter-robot separation, triggering re-planning when safety margins are violated. Extensive MATLAB simulations across cluttered 3D scenarios demonstrate that IMD--TAPP consistently produces dynamically feasible, collision-free trajectories while achieving competitive completion times. In a representative case study with two drones serving multiple goals, the proposed approach attains a minimum mission time of 136~s while maintaining the required safety constraints throughout execution.
Fly360: Omnidirectional Obstacle Avoidance within Drone View
Institute of Automation Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；Insta360 Research, Shenzhen, China（Insta360研究院）；Wuhan University,Wuhan, China（武汉大学）
EcoFlight: Finding Low-Energy Paths Through Obstacles for Autonomous Sensing Drones
Computer Science New Jersey Institute of Technology Newark, NJ, USA（计算机科学 新 jersey 理工学院 新ARK, NJ, USA）；Computer Engineering New York Institute of Technology New York, NY, USA（电子与计算机工程 新 york 理工学院 新 YORK, NY, USA）
Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
School of Automation and Perception, Shanghai Jiao Tong University（自动化与感知学院，上海交通大学）；Faculty of Aerospace Engineering, Delft University of Technology（航空航天工程学院，代尔夫特理工大学）
Bumper Drone: Elastic Morphology Design for Aerial Physical Interaction
University of Bristol（布里斯托大学）；School of Engineering Mathematics and Technology（工程数学与技术学院）；School of Civil, Aerospace and Design Engineering（土木、航空航天与设计工程学院）；Department of Aeronautics（航空系）；Laboratory of Sustainability Robotics, EMPA（可持续机器人实验室，EMPA）
Droneulator: A Portable UAV Simulator for Agricultural Workflows with RotorPy and Godot 4
Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln, Lincoln, UK（林肯自主系统中心（L-CAS）、林肯大学、林肯，英国）；Faculty of Aerospace Engineering, MAVLab, Delft University of Technology (TU Delft), Delft, Netherlands（航空航天工程学院、MAVLab、代尔夫特理工大学（TU Delft）、代尔夫特，荷兰）
Exploring Unstructured Environments using Minimal Sensing on Cooperative Nano-Drones
Computer Vision and Aerial Robotics Group at Universidad Politécnica de Madrid（马德里理工大学计算机视觉与空中机器人小组）；Centre for Automation and Robotics C.A.R. (UPM-CSIC)（自动化与机器人中心（UPM-CSIC））；Department of Mechanical Engineering, Texas A&M University（机械工程系，德克萨斯A&M大学）
Tombo Propeller: Bio-Inspired Deformable Structure toward Collision-Accommodated Control for Drones
Soft Haptics Lab., School of Materials Science, Japan Advanced Institute of Science and Technology（日本先进科学技术学院材料科学学院软触觉实验室）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Japan Science and Technology Agency, PRESTO（日本科学技术厅）
Multi-sensory Anti-collision Design for Autonomous Nano-swarm Exploration
University of Bologna（博洛尼亚大学）；KU Leuven（鲁汶大学）；ETH Zürich（苏黎世联邦理工学院）；Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI（USI-SUPSI 达尔·莫勒人工智能研究所（IDSIA））
Distance-based Multiple Non-cooperative Ground Target Encirclement for Complex Environments
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）
A Cooperative Aerial System of A Payload Drone Equipped with Dexterous Rappelling End Droid for Cluttered Space Pickup
School of Aerospace Science and Engineering, Beihang University（北京航空航天大学航空航天科学与工程学院）；Hangzhou International Innovation Institute of Beihang University（北京航空航天大学杭州国际创新研究院）；Institute of Unmanned System, Beihang University（北京航空航天大学无人系统研究院）；Tianmushan Laboratory（天门山实验室）
