The Stanford testbed of autonomous rotorcraft for multi agent control (STARMAC)

Hoffmann, Gabriel; Rajnarayan, Dev; Waslander, Steven L.; Dostal, D.; Jang, Jung Soon; Tomlin, Claire J.

doi:10.1109/dasc.2004.1390847

Cited by 189 publications

(117 citation statements)

References 8 publications

(8 reference statements)

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“…Hoffmann, Rajnarayan et al [4] developed STARMAC and STARMAC II at Standford University. The Stanford platform was also used for Multi-UAV Experiments.…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz

Gurdan

Stumpf

Achtelik

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

208

137

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Abstract-We describe an efficient, reliable, and robust fourrotor flying platform for indoor and outdoor navigation. Currently, similar platforms are controlled at low frequencies due to hardware and software limitations. This causes uncertainty in position control and instable behavior during fast maneuvers. Our flying platform offers a 1 kHz control frequency and motor update rate, in combination with powerful brushless DC motors in a light-weight package. Following a minimalistic design approach this system is based on a small number of lowcost components. Its robust performance is achieved by using simple but reliable highly optimized algorithms. The robot is small, light, and can carry payloads of up to 350g.

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“…Hoffmann, Rajnarayan et al [4] developed STARMAC and STARMAC II at Standford University. The Stanford platform was also used for Multi-UAV Experiments.…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz

Gurdan

Stumpf

Achtelik

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

208

137

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“…[4][5][6], many research groups have used a variety of platforms to verify advanced theories and approaches in the development of innovative UAV concepts as presented in Ref. [7][8][9]. While pure simulation environments are very convenient, they typically do not enforce key real-world implementation challenges such as vehicle dynamics, determinism, communication delays, discretization effects, noise, processing latency, etc.…”

Section: Rapid Prototypingmentioning

confidence: 99%

Vehicle Swarm Rapid Prototyping Testbed

Saad

Vian

Clark

et al. 2009

AIAA Infotech@Aerospace Conference

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Increased levels of vehicle collaboration and autonomy are seen as a means to reduce overall mission completion costs while expanding mission capabilities and increasing mission assurance for complex coupled system of systems. Systems health management technologies have made rapid advances that enable systems to know their own condition and capabilities, thus creating the opportunity for unprecedented levels of adaptive control, real-time reconfiguration, and mission contingency management. Multi-agent task allocation and mission managements systems must account for vehicle-and system-level health-related issues to ensure that these systems are reliable and cost effective to operate.

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“…Due to these applications, and because of their relatively small size, ability to hover, and mechanical simplicity, quadrotor helicopter UAVs are a popular choice among researchers in control and robotics (Hoffmann et al, 2004;How et al, 2008;Michael et al, 2010;Huang et al, 2011a;Lupashin et al, 2011;Meier et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

Bouffard¹

2012

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE DEC 20122. REPORT TYPE 3. DATES COVERED 00-00-2012 to 00-00-2012 Copyright © 2012, by the author(s). TITLE AND SUBTITLE On-board Model Predictive Control of a QuadrotorAll rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. AcknowledgementThe This report describes work in applying model predictive control (MPC) techniques to the control of quadrotor helicopters, a type of micro aerial vehicle (MAV) platform that has gained great popularity in recent years both in research and commercial/military settings. MPC is a form of optimal control which is attractive in part because it allows engineering requirements to be addressed directly in the design of the controller in terms of costs to be minimized and constraints to be satisfied in an optimization problem. Furthermore, for many engineering problems of interest, the optimization to be performed is convex, meaning that a global optimum can be efficiently computed. MPC first found broad early application in the process industry, where the typically longer time scales were compatible with the time necessary to solve the optimization problem. More recently with both the exponential increase in available computing power and the development of more efficient solution techniques, MPC has become an option for control of systems with faster dynamics, such as quadrotors.We bring together results from our application of two distinct variants of MPC. The common thread is that we seek advanced control algorithms that can be applied to an autonomous MAV like the quadrotor, ideally without requiring any external resources, i.e. we aim to perform all computations required for real-time closed-loop control on-board the vehicle.The first variant is known as explicit MPC, where in a sense the heavy numerical work of solving optimization problems is done a priori and off-line, such that the on-line implementat...

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The Stanford testbed of autonomous rotorcraft for multi agent control (STARMAC)

Cited by 189 publications

References 8 publications

Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz

Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz

Vehicle Swarm Rapid Prototyping Testbed

On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments

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