This paper presents a novel on-line path planning method that enables aerial robots to interact with surfaces. We present a solution to the problem of finding trajectories that drive a robot towards a surface and move along it. Triangular meshes are used as a surface map representation that is free of fixed discretization and allows for very large workspaces. We propose to leverage planar parametrization methods to obtain a lower-dimensional topologically equivalent representation of the original surface. Furthermore, we interpret the original surface and its lower-dimensional representation as manifold approximations that allow the use of Riemannian Motion Policies (RMPs), resulting in an efficient, versatile, and elegant motion generation framework. We compare against several Rapidly-exploring Random Tree (RRT) planners, a customized CHOMP variant, and the discrete geodesic algorithm. Using extensive simulations on real-world data we show that the proposed planner can reliably plan high-quality near-optimal trajectories at minimal computational cost. The accompanying multimedia attachment demonstrates feasibility on a real OMAV. The obtained paths show less than 10% deviation from the theoretical optimum while facilitating reactive re-planning at kHz refresh rates, enabling flying robots to perform motion planning for interaction with complex surfaces.
Robust Planning and Control of Omnidirectional MRAVs for Aerial Communications in Wireless Networks
Ricerca sul Sistema Energetico（能源系统研究）；Czech Technical University in Prague（布拉格捷克技术大学）；College of Computing, Mohammed VI Polytechnic University（穆莱·伊斯梅尔polytechnic大学计算机学院）
Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots
DRAGON Lab, Department of Mechanical Engineering, The University of Tokyo（DRAGON实验室，机械工程系，东京大学）；Department of Advanced Interdisciplinary Studies, The University of Tokyo（先进跨学科研究系，东京大学）
Omni-Perception: Omnidirectional Collision Avoidance for Legged Locomotion in Dynamic Environments
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Spatialtemporal AI（时空AI）；The Hong Kong University of Science and Technology（香港科技大学）；Department of Eletronic Engineering, Tsinghua University（清华大学电子工程系）；Beijing Innovation Center of Humanoid Robotics Co., Ltd.（北京人形机器人创新中心有限公司）
Reshaping UAV-Enabled Communications with Omnidirectional Multi-Rotor Aerial Vehicles
College of Computing, Mohammed VI Polytechnic University（穆罕默德六世理工大学计算机学院）；Czech Technical University in Prague（捷克布拉格技术大学）；International University of Rabat（拉巴特国际大学）
Safety-Critical Bilateral Teleoperation for Omnidirectional Aerial Manipulation Using Force-Sensorless Haptic Feedback
Seoul National University（首尔大学）；Automation and System Research Institute (ASRI)（自动化与系统研究所）；Institute of Advanced Aerospace Technology (IAAT)（先进航空航天技术研究所）
Omnidirectional Dual-Arm Aerial Manipulator with Proprioceptive Contact Localization for Landing on Slanted Roofs
BioMorphic Intelligence Lab, Dept. Control & Operations, Faculty of Aerospace Engineering, TU Delft（德鲁特技术大学航空航天工程学院控制与运营系生物形态智能实验室）；School of Engineering Mathematics and Technology, and Bristol Robotics Laboratory, University of Bristol（布里斯托大学工程数学与技术学院及布里斯托机器人实验室）
Generating 6-D Trajectories for Omnidirectional Multirotor Aerial Vehicles in Cluttered Environments
School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳）机械工程与自动化学院）；Faculty of Aerospace Engineering, Delft University of Technology（代尔夫特理工大学航空航天工程学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees
Department of Cybernetics, Czech Technical University（捷克技术大学控制学系）；Department of Cognitive Robotics, Delft University of Technology（代尔夫特理工大学认知机器人学系）；Leiden Institute of Advanced Computer Science, Leiden University（莱顿大学高级计算机科学研究所）
Local Path Planning and Obstacle Avoidance for an Omnicopter Platform
Faculty of Aerospace Engineering, Delft University of Technology（代尔夫特理工大学航空航天工程学院）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Center for Artificial Intelligence and Robotics, New York University Abu Dhabi（纽约大学阿布扎比分校人工智能与机器人中心）
UAV See, UGV Do: Aerial Imagery and Virtual Teach Enabling Zero-Shot Ground Vehicle Repeat
University of Toronto Robotics Institute（多伦多大学机器人研究所）；University of Toronto Institute of Aerospace Studies（多伦多大学航空航天研究学院）；Ingenuity Labs（Ingenuity实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees
Universidad Politécnica de Madrid（西班牙马德里理工大学）；School of Science and Technology, IE University（IE大学科学与技术学院）；Universidad de San Andrés (UdeSA) and CONICET（阿根廷圣安德烈斯大学（UdeSA）和CONICET）；Centre for Automation and Robotics (CSIC-UPM)（自动化与机器人中心（CSIC-UPM））
