Position-based visual servo control of leader-follower formation using image-based relative pose and relative velocity estimation

Dani, Ashwin P.; Gans, Nicholas; Dixon, Warren E.

doi:10.1109/acc.2009.5160698

Cited by 23 publications

(24 citation statements)

References 22 publications

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“…remain bounded. However, a comparison of p 22 obtained from the solution of Eqn (18) with that obtained from the solution of Eqn (5) shows some mismatch of the two poles, particularly in the region surrounding t=3 sec as well as close to t=5sec. A modified optimization function, that now involves the use of two measurement sources (i.e.…”

Section: Determination Of Ltv Poles From Output Measurements Through mentioning

confidence: 94%

Research Institute for Autonomous Precision Guided Systems

Rogacki¹,

Ukeiley²,

Dixon³

et al. 2008

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The public reporting burden for this 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 the burden, to Department of Defense. Washington Headquarters Services. Directorate for Information Under the leadership of Dr. Rogacki and Dr. Albertani there was significant progress made in delivering a hardware-in-the-loop rapid prototyping capability at the UF-REEF. Embedded systems are designed to control complex plants such as land vehicles, satellites, spacecrafts, unmanned aerial vehicles (UAVs), aircrafts, weapon systems, marine vehicles, and jet engines. They generally require a high level of complexity within the embedded system to manage the complexity of the plant under control. Hardware-in-the-Loop (HIL) simulation is a technique that is used increasingly in the development and test of complex real-time embedded systems.The purpose of HIL simulation is to provide an effective platform for developing and testing real-time embedded systems. HIL simulation provides an effective platform by adding the complexity of the plant under control to the test platform. The complexity of the plant under control is included in test and development by adding a mathematical representation of all related dynamic systems. These mathematical representations are referred to as the "plant simulation.'" The UF-REEF has established a HIL simulation capability for MAV's that will lead to a rapid prototyping capability.The Task 1 research plan included testing the visual based control system running on the cluster PCs in the REEF Visualization Lab by flying a relatively stable off the shelf slow flier (similar to MAV flight regime) in the small REEF wind tunnel and later in the large wind tunnel. The general layout of the test bed is illustrated in Fig. 2.

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Section: Determination Of Ltv Poles From Output Measurements Through mentioning

confidence: 94%

Research Institute for Autonomous Precision Guided Systems

Rogacki¹,

Ukeiley²,

Dixon³

et al. 2008

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“…The approaches used in leader-follower methods often require measurement of the velocity of the leader robot. Some papers address this by using estimation methods [21], [22] or nonlinear observers [23].…”

Section: Introductionmentioning

confidence: 99%

Formation control of wheeled robots with vision-based position measurement

Poonawala¹,

Satici²,

Gans³

et al. 2012

2012 American Control Conference (ACC)

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Many applications require multiple mobile robots to move with a common velocity and at fixed relative distances. We present a time-invariant, state-feedback control law and a novel vision-based pose reconstruction system that allows one differential drive robot to follow another at a constant relative distance. The control law does not require measurement or estimation of the leader robot velocity and has tunable parameters that allows one to prioritize the error bounds of the desired states. The proposed pose reconstruction algorithm is computationally inexpensive and reliable. We present experimental results on two iRobot Create robots showing the performance of the controller and vision algorithm.

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“…The relative pose is measured by the on-board laser sensor, and the unknown leader's motion was estimated by a novel observer. Dani et al [2] and Poonawala et al [3] measured the relative pose by pose reconstruction using a perspective camera. Dani et al [2] estimated the relative velocity using a nonlinear estimator.…”

Section: Introductionmentioning

confidence: 99%

“…Dani et al [2] and Poonawala et al [3] measured the relative pose by pose reconstruction using a perspective camera. Dani et al [2] estimated the relative velocity using a nonlinear estimator. Poonawala et al [3] eliminated the need of leader's velocity in the controller, so the design of the observer to estimate the leader's motion could be avoided.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Image-Based Leader-Follower Approach of Mobile Robot with Omnidirectional Camera

Guo

Wang

Chen

et al. 2015

Journal of Applied Mathematics

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This paper focuses on the problem of the adaptive image-based leader-follower formation control of mobile robot with onboard omnidirectional camera. A calibrated omnidirectional camera is fixed on the follower in any position, and a feature point representing the leader can be chosen in any position. An adaptive image-based controller without depending on the velocity of the leader is proposed based on a filter technology. In other words, only by relying on the projection of the feature on the image plane, can the follower track the leader and achieve the formation control. Moreover, an observer is introduced to estimate the unknown camera extrinsic parameters and the unknown parameters of plane, where the feature point moves, relative to omnidirectional camera frame. At last, the lyapunov method is applied to prove the uniform semiglobal practical asymptotic stability (USPAS) for the closed-loop system. Simulation results are presented to validate the algorithm.

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Position-based visual servo control of leader-follower formation using image-based relative pose and relative velocity estimation

Cited by 23 publications

References 22 publications

Research Institute for Autonomous Precision Guided Systems

Research Institute for Autonomous Precision Guided Systems

Formation control of wheeled robots with vision-based position measurement

Adaptive Image-Based Leader-Follower Approach of Mobile Robot with Omnidirectional Camera

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