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Hold Or take Optimal Plan (HOOP): A quadratic programming approach to multi-robot trajectory generation

INTERNATIONAL JOURNAL OF robotics RESEARCH 2018年第9期37卷 1062-1084页

作者： Tang, Sarah Thomas, Justin Kumar, Vijay Univ Penn Gen Robot Automat Sensing & Percept GRASP Lab Philadelphia PA 19104 USA

In this work, we present Hold Or take Optimal Plan (HOOP), a centralized trajectory generation algorithm for labeled multi-robot systems operating in obstacle-free, two-dimensional, continuous workspaces. Given a team of N robots, each with nth-order dynamics, our algorithm finds trajectories that navigate vehicles from their start positions to non-interchangeable goal positions in a collision-free manner. The algorithm operates in two phases. In the motion planning step, a geometric algorithm finds a collision-free, piecewise-linear trajectory for each robot. In the trajectory generation step, each robot's trajectory is refined into a higher-order piecewise polynomial with a quadratic program. The novelty of our method is in this problem decomposition. The motion plan, through abstracting away robots' dynamics, can be found quickly. It is then subsequently leveraged to construct collision avoidance constraints for N decoupled quadratic programs instead of a single, coupled optimization problem, decreasing computation time. We prove that this method is safe, complete, and generates smooth trajectories that respect robots' dynamics. We demonstrate the algorithm's practicality through extensive quadrotor experiments.

关键词： Multirobot planning aerial robotics motion control

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A General Purpose Configurable Controller for Indoors and Outdoors GPS-Denied Navigation for Multirotor Unmanned aerial Vehicles

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2014年第1-4期73卷 387-400页

作者： Pestana, Jesus Mellado-Bataller, Ignacio Sanchez-Lopez, Jose Luis Fu, Changhong Mondragon, Ivan F. Campoy, Pascual CSIC UPM Ctr Automat & Robot Madrid Spain Pontificia Univ Javeriana Ctr Tecnol Automatizac Ind Bogota 110311 Colombia

This research on odometry based GPS-denied navigation on multirotor Unmanned aerial Vehicles is focused among the interactions between the odometry sensors and the navigation controller. More precisely, we present a controller architecture that allows to specify a speed specified flight envelope where the quality of the odometry measurements is guaranteed. The controller utilizes a simple point mass kinematic model, described by a set of configurable parameters, to generate a complying speed plan. For experimental testing, we have used down-facing camera optical-flow as odometry measurement. This work is a continuation of prior research to outdoors environments using an AR Drone 2.0 vehicle, as it provides reliable optical flow on a wide range of flying conditions and floor textures. Our experiments show that the architecture is realiable for outdoors flight on altitudes lower than 9 m. A prior version of our code was utilized to compete in the International Micro Air Vehicle Conference and Flight Competition IMAV 2012. The code will be released as an open-source ROS stack hosted on GitHub.

关键词： aerial robotics Autonomous navigation Computer vision Control architectures and programming Control of UAVs Micro aerial vehicles Multirotor modeling and state estimation robotics Unmanned aerial vehicles

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The GRASP Multiple Micro-UAV Test Bed Experimental Evaluation of Multirobot aerial Control Algorithms

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IEEE robotics & AUTOMATION MAGAZINE 2010年第3期17卷 56-65页

作者： Michael, Nathan Mellinger, Daniel Lindsey, Quentin Kumar, Vijay Univ Penn GRASP Lab Philadelphia PA 19104 USA

In the last five years, advances in materials, electronics, sensors, and batteries have fueled a growth in the development of microunmanned aerial vehicles (MAVs) that are between 0.1 and 0.5 m in length and 0.1-0.5 kg in mass [1]. A few groups have built and analyzed MAVs in the 10-cm range [2], [3]. One of the smallest MAV is the Picoflyer with a 60-mm propellor diameter and a mass of 3.3 g [4]. Platforms in the 50-cm range are more prevalent with several groups having built and flown systems of this size [5]-[7]. In fact, there are several commercially available radiocontrolled (RC) helicopters and research-grade helicopters in this size range [8].

关键词： Experimental robotics multirobot control aerial robotics testbed design

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Learning to soar: Resource-constrained exploration in reinforcement learning

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2015年第2期34卷 158-172页

作者： Chung, Jen Jen Lawrance, Nicholas R. J. Sukkarieh, Salah Univ Sydney Sch Aerosp Mech & Mechatron Engn Australian Ctr Field Robot Sydney NSW 2006 Australia

This paper examines temporal difference reinforcement learning with adaptive and directed exploration for resource-limited missions. The scenario considered is that of an unpowered aerial glider learning to perform energy-gaining flight trajectories in a thermal updraft. The presented algorithm, eGP-SARSA(), uses a Gaussian process regression model to estimate the value function in a reinforcement learning framework. The Gaussian process also provides a variance on these estimates that is used to measure the contribution of future observations to the Gaussian process value function model in terms of information gain. To avoid myopic exploration we developed a resource-weighted objective function that combines an estimate of the future information gain using an action rollout with the estimated value function to generate directed explorative action sequences. A number of modifications and computational speed-ups to the algorithm are presented along with a standard GP-SARSA() implementation with epsilon -greedy exploration to compare the respective learning performances. The results show that under this objective function, the learning agent is able to continue exploring for better state-action trajectories when platform energy is high and follow conservative energy-gaining trajectories when platform energy is low.

关键词： Reinforcement learning exploration informative planning aerial robotics autonomous soaring

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Autonomous tracking and landing of an unmanned aerial vehicle on a ground vehicle in rough terrain

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ADVANCED robotics 2023年第5期37卷 344-355页

作者： Aoki, Nobuaki Ishigami, Genya Keio Univ Grad Sch Sci & Technol Yokohama Kanagawa Japan Keio Univ Dept Mech Engn Yokohama Kanagawa Japan

Collaborative exploration using unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has become increasingly popular in academia over the past decade. This study addresses the task of autonomously landing a UAV on a ground vehicle in rough terrain, which is fundamental, yet challenging, for UAV-UGV collaborative exploration. The method proposed in this paper estimates the relative pose and velocity between UAV and UGV and uses model predictive control for UAV trajectory planning while considering the field of view of camera onboard the UAV. Through a simulation study, we confirmed that the proposed method enables a UAV to accurately track the UGV traversing in rough terrain, and to land on it with 100% success rate at the UGV velocity of 1.0 m/s and still 50% at 3.0 m/s.

关键词： aerial robotics control planning position estimation collaborative exploration

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Modeling Synchronous Muscle Function in Insect Flight: a Bio-Inspired Approach to Force Control in Flapping-Wing MAVs

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2013年第1-4期70卷 181-202页

作者： Mahjoubi, Hosein Byl, Katie Univ Calif Santa Barbara Dept Elect & Comp Engn Robot Lab Santa Barbara CA 93106 USA

Micro-aerial vehicles (MAV) and their promising applications-such as undetected surveillance or exploration of environments with little space for land-based maneuvers-are a well-known topic in the field of aerial robotics. Inspired by high maneuverability and agile flight of insects, over the past two decades a significant amount of effort has been dedicated to research on flapping-wing MAVs, most of which aim to address unique challenges in morphological construction, force production, and control strategy. Although remarkable solutions have been found for sufficient lift generation, effective methods for motion control still remain an open problem. The focus of this paper is to investigate general flight control mechanisms that are potentially used by real insects, thereby providing inspirations for flapping-wing MAV control. Through modeling the insect flight muscles, we show that stiffness and set point of the wing's joint can be respectively tuned to regulate the wing's lift and thrust forces. Therefore, employing a suitable controller with variable impedance actuators at each wing joint is a prospective approach to agile flight control of insect-inspired MAVs. The results of simulated flight experiments with one such controller are provided and support our claim.

关键词： Bio-inspired MAVs aerial robotics Insect flight Tunable impedance Variable stiffness actuator Maneuverability

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Photometric-Planner for Visual Path Following

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IEEE SENSORS JOURNAL 2021年第10期21卷 11310-11317页

作者： A. Rodriguez Martinez, Eder Caron, Guillaume Pegard, Claude Lara-Alabazares, David Univ Picardie Jules Verne Lab Modeling Informat & Syst F-80025 Amiens France Natl Technol Inst Mexico Postgrad Dept Misantla 93821 Mexico

Robotic navigation is the aspect of cognition related to robot robust mobility. It combines perception, some knowledge on the environment and a set of goal poses to reliably control the robot during a mission that involves displacement. Vision-based autonomous navigation is an instantiation of the latter discipline where visual perception is used to control the robot and to represent the environment. This article presents a vision-based navigation system that uses its onboard camera to navigate and a visual path that represents the scene with a set of images. Being a memory-based system, the navigation is conceived as a concatenation of positioning tasks in the visual servoing scheme. The novelty on the proposed system relies on the generation of the images that compose the visual path. These images are rendered from a preobtained model of the scene. The experiments evaluate the performance of the system over three different scenes, contemplating indoor and outdoor environments, and using an Unmanned aerial Vehicle as testbed.

关键词： Vision-based robotic navigation direct visual servoing aerial robotics

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Global estimation in constrained environments

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2012年第1期31卷 24-41页

作者： Kobilarov, Marin Marsden, Jerrold E. Sukhatme, Gaurav S. CALTECH Pasadena CA 91125 USA Univ So Calif Los Angeles CA USA

This article considers the optimal estimation of the state of a dynamic observable using a mobile sensor. The main goal is to compute a sensor trajectory that minimizes the estimation error over a given time horizon taking into account uncertainties in the observable dynamics and sensing, and respecting the constraints of the workspace. The main contribution is a methodology for handling arbitrary dynamics, noise models, and environment constraints in a global optimization framework. It is based on sequential Monte Carlo methods and sampling-based motion planning. Three variance reduction techniques-utility sampling, shuffling, and pruning-based on importance sampling, are proposed to speed up convergence. The developed framework is applied to two typical scenarios: a simple vehicle operating in a planar polygonal obstacle environment and a simulated helicopter searching for a moving target in a 3-D terrain.

关键词： aerial robotics motion planning estimation search and rescue robots

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An autonomous unmanned aerial vehicle system for fast exploration of large complex indoor environments

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JOURNAL OF FIELD robotics 2021年第8期38卷 1036-1058页

作者： Kratky, Vit Petracek, Pavel Baca, Tomas Saska, Martin Czech Tech Univ Fac Elect Engn Dept Cybernet Prague Czech Republic

This paper introduces an autonomous system employing multirotor unmanned aerial vehicles for fast 3D exploration and inspection of vast, unknown, dynamic, and complex environments containing large open spaces as well as narrow passages. The system exploits the advantage of small-size aerial vehicles capable of carrying all necessary sensors and computational power while providing full autonomy and mobility in constrained unknown environments. Particular emphasis is put on the robustness of the algorithms with respect to challenging real-world conditions and the real-time performance of all algorithms that enable fast reactions to changes in environment and thus also provide effective use of limited flight time. The system presented here was employed as a part of a heterogeneous ground and aerial system in the modeled Search & Rescue scenario in an unfinished nuclear power plant during the Urban Circuit of the Subterranean Challenge (SubT Challenge) organized by the Defense Advanced Research Projects Agency. The main goal of this simulated disastrous scenario is to autonomously explore and precisely localize specified objects in a completely unknown environment and to report their position before the end of the mission. The proposed system was part of the multirobot team that finished in third place overall and in first place among the self-funded teams. The proposed unmanned aerial vehicle system outperformed all aerial systems participating in the SubT Challenge with respect to versatility, and it was also the self-deployable autonomous aerial system that explored the largest part of the environment.

关键词： aerial robotics exploration extreme environments GPS-denied operation planning

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A visual odometer for autonomous helicopter flight

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robotics AND AUTONOMOUS SYSTEMS 1999年第2-3期28卷 185-193页

作者： Amidi, O Kanade, T Fujita, K Carnegie Mellon Robot Inst Pittsburgh PA USA

This paper presents a visual odometer for autonomous helicopter flight. The odometer estimates helicopter position by visually locking on to and tracking ground objects. The paper describes the philosophy behind the odometer as well as its tracking algorithm and implementation The paper concludes by presenting test flight data of the odometer's performance on-board indoor and outdoor prototype autonomous helicopters. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

关键词： helicopter vision-based odometer navigation aerial robotics

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