Real-Time Model-Based Vision System For Object Acquisition And Tracking

Wilcox, Brian; Gennery, Donald B.; Bon, B.; Litwin, Todd

doi:10.1117/12.939994

Cited by 11 publications

(7 citation statements)

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“…where the variables are defined as 1 The total wrench, twist, and mass matrix are defined as: 1) F e = (f e , τ e ) ∈ R 6 the wrench; 2) V s = (v s , ω s ) ∈ R 6 the twist; 3) H = (M , 0 3×3 ; 0 3×3 , I) ∈ R 6×6 the total mass matrix (mass and inertia of the body). By integrating (1) and (2) and by applying the inverse kinematics it is possible to obtain the desired joint velocities for the industrial robot to track and the end-effector will evolve as a floating rigid body not subjected to gravity. Fig.…”

Section: Problem Statementmentioning

confidence: 99%

“…Advances in space robotic technologies have enabled the execution of precise and complex maintenance tasks in orbit which reduce the risks associated with manned extravehicular activities. Unmanned mission demonstrators were flown in orbit to validate autonomous robot control technologies [2]. A milestone was the success of the Robot Technology Experiment (ROTEX) to demonstrate servicing prototype capabilities using robots [3].…”

Section: Introductionmentioning

confidence: 99%

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A Passivity-Based Approach for Simulating Satellite Dynamics With Robots: Discrete-Time Integration and Time-Delay Compensation

2020

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This article proposes a passivity-based approach for simulating satellite dynamics on a position-controlled robot equipped with a force-torque sensor. Time delays intrinsic in the computational loop and discrete-time integration degrade the behavior of the satellite dynamics reproduced by the robot. These factors can generate an energy-inconsistent simulation and can even render the system unstable. In this article, time delay and discrete-time integration effects are analyzed from an energetic perspective and compensated through a passivity-based control strategy to ensure a faithful and stable dynamic simulation with position-controlled robots. The benefit of the proposed strategy is validated by simulations and experiments on the On-Orbit Servicing Simulator (OOS-SIM), a robotic facility used for simulating free-floating dynamics.

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Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Passivity-Based Approach for Simulating Satellite Dynamics With Robots: Discrete-Time Integration and Time-Delay Compensation

2020

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“…Previous work in aufrom stereo sensors [9,10] at a given instant of time for pose determination. In our work we consider the autonomous docking with spin stabilized satellites, which rotate about a spin axis (nominally pointed at the sun) at less than 50 r.p.m.…”

Section: Introductionmentioning

confidence: 99%

<title>Optical neural network system for pose determination of spinning satellites</title>

Lee

Casasent

1990

SPIE Proceedings

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An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning sateffites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (sateffite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the effiptical path of a part in image space, the 3-D pose of the satellite is determined. Digital simulation results using this algorithm are presented for various sateffite poses and lighting conditions.

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“…Secondly, the attributed graph representation and subgraph matching proposed here avoids the computational effort and problems inherent in the probablistic search associated with the matching at the feature Level [30].…”

Section: I43mentioning

confidence: 99%

“…Active research in vision information processing is being pursued at various centers in this country, and has been widely documented in the literature (see, e.g., [12]-[17], [30] and the references therein). In what follows, we highlight the basic philosophy and thrust of some of the algorithmic work currently in progress at Rice University.…”

Section: Algorithms For Informalion Processingmentioning

confidence: 99%

Robotic vision/Sensing for space applications

Krishen

Figueiredo²,

Graham³

Proceedings. 1987 IEEE International Conference on Robotics and Automation

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In the past decade, visionisensing systems techniques have received significant attention for applications in robotics and automation. The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of tasks unsuited to humans. These benefits can be insured through automating time-consuming tasks, increasing productivityiperfornce of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress at the WASNJohnson Space Center and at Rice University, the accomplishments to date, and some of the anticipated future developments. Both systems and algorithms are discussed. The evolution of future visionisensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters.The algorithms for information extraction would incorporate aspects of intelligence and knowledge for the interpolation and extrapolation of the needed data. The key features of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspeces of visionisensing will also be discussed in this paper.

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Real-Time Model-Based Vision System For Object Acquisition And Tracking

Cited by 11 publications

References 0 publications

A Passivity-Based Approach for Simulating Satellite Dynamics With Robots: Discrete-Time Integration and Time-Delay Compensation

A Passivity-Based Approach for Simulating Satellite Dynamics With Robots: Discrete-Time Integration and Time-Delay Compensation

<title>Optical neural network system for pose determination of spinning satellites</title>

Robotic vision/Sensing for space applications

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