A competition to identify key challenges for unmanned aerial robots in near-earth environments

Green, W.E.; Sevcik, Keith W.; Oh, Paul

doi:10.1109/icar.2005.1507429

Cited by 16 publications

(12 citation statements)

References 10 publications

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“…Therefore, dynamics and motion control of SFFR have been studied extensively, [1][2][3][4]. Flying capability opens new opportunities in terrestrial applications as well, to perform field services and tasks like search and rescue, observation and mapping, [5][6][7]]. An Aerial Robot or unmanned aerial vehicle (UAV) may be defined as an aerial vehicle (mostly without on-board manipulators) that uses aerodynamic forces to support its flight in a desired manner, so that a modem UAV is a fully autonomous flying system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of an Aerial Robot using Lyapunov Design

Zarafshan

Moosavian

Bahrami

2008

2008 IEEE Conference on Robotics, Automation and Mechatronics

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In this article, based on feedback linearization using Lyapunov design method, an adaptive controller is proposed for an Aerial Robot or unmanned aerial vehicle (UAV). After introducing a nonlinear dynamics model of the system in case of longitudinal equations, comparing controllers are designed to manage the system performance during various maneuvers. Also, stability analysis for the designed adaptation law is studied and discussed. To evaluate the performance of designed controllers for a given system, a comprehensive simulation program is developed. One of the most important results of this study is that tracking errors for the two state variables exponentially converge to zero, even in the presence of parameters uncertainty. Therefore, it is shown that the proposed adaptive controller is able to perform perfect path tracking maneuvers.

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Section: Introductionmentioning

confidence: 99%

Adaptive Control of an Aerial Robot using Lyapunov Design

Zarafshan

Moosavian

Bahrami

2008

2008 IEEE Conference on Robotics, Automation and Mechatronics

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“…Instead, it learns the control policy online and does not require a-priori information about the payload, the temperature, or the air pressure. Furthermore, autonomous blimps have been presented in different application scenarios, for instance by Jung and Lacroix [10], Fukao et al [5], and Green et al [7].…”

Section: Related Workmentioning

confidence: 99%

Autonomous blimp control using model-free reinforcement learning in a continuous state and action space

Rottmann

Plagemann

Hilgers

et al. 2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to previous approaches, our method does not require sophisticated handtuned models, but rather learns the policy online, which makes the system easily adaptable to changing conditions. The blimp we apply our approach to is a small-scale vehicle equipped with an ultrasound sensor that measures its elevation relative to the ground. The major problem in the context of learning control policies lies in the high-dimensional state-action space that needs to be explored in order to identify the values of all state-action pairs. In this paper, we propose a solution to learning continuous control policies based on the Gaussian process model. In practical experiments carried out on a real robot we demonstrate that the system is able to learn a policy online within a few minutes only.

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“…Green and colleagues [8] used an infrared sensor to detect obstacles: when the collision avoidance system detects an obstacle, the blimp turned 180 degrees to avoid collision. The collision avoidance system does not take account of blimp dynamics, however; consequently, we used an ultrasonic sensor to measure the distance to potential obstacles and implemented a fuzzy logic controller to avoid collisions.…”

Section: Related Workmentioning

confidence: 99%

Developing a low-cost autonomous indoor blimp

González¹,

Burgard²,

Domínguez³

et al. 2009

jopha

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Abstract-This paper describes the design of an autonomous blimp-based robot and its navigation system. The robot was based on a commercial kit and its dimensions were suitable for use in indoor environments. Our main goal was to develop a simple and safe model for evaluating different autonomously controlled navigation techniques. Due to the special requirements of this application, two specific electronics boards to control the blimp and to communicate with the PC ground station were designed and two different altitude controllers and also a controller to maintain distance from obstacles were implemented. Finally, comparative results on both altitude controllers are presented.

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A competition to identify key challenges for unmanned aerial robots in near-earth environments

Cited by 16 publications

References 10 publications

Adaptive Control of an Aerial Robot using Lyapunov Design

Adaptive Control of an Aerial Robot using Lyapunov Design

Autonomous blimp control using model-free reinforcement learning in a continuous state and action space

Developing a low-cost autonomous indoor blimp

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