Accurate knowledge of depth continues to be of critical importance in robotic systems. Without accurate depth knowledge, tasks such as inspection, tracking, grasping, and collision-free motion planning prove to be dif...
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Accurate knowledge of depth continues to be of critical importance in robotic systems. Without accurate depth knowledge, tasks such as inspection, tracking, grasping, and collision-free motion planning prove to be difficult and often unattainable. Traditional visual depth recovery has relied upon techniques that require the solution of the correspondence problem or require known lighting conditions and Lambertian surfaces. In this paper, we present a technique for the derivation of depth from feature points on a target's surface using the controlled active vision framework. We use a single visual sensor mounted on the end-effector of a robotic manipulator to automatically select feature points and to derive depth estimates for those features using adaptive control techniques. Movements of the manipulator produce displacements that are measured using a sum-of-squared difference (SSD) optical flow. The measured displacements are fed into the controller to alter the path of the manipulator and to refine the depth estimate.< >
Flexible operation of a robotic agent in an uncalibrated environment requires the ability to recover unknown or partially known parameters of the workspace through sensing. Of the sensors available to a robotic agent,...
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Flexible operation of a robotic agent in an uncalibrated environment requires the ability to recover unknown or partially known parameters of the workspace through sensing. Of the sensors available to a robotic agent, visual sensors provide information that is richer and more complete than other sensors. In this paper we present robust techniques for the derivation of depth from feature points on a target's surface and for the accurate and high-speed tracking of moving targets. We use these techniques in a system that operates with little or no a priori knowledge of the object-related parameters present in the environment. The system is designed under the controlled active vision framework and robustly determines parameters such as velocity for tracking moving objects and depth maps of objects with unknown depths and surface structure. Such determination of intrinsic environmental parameters is essential for performing higher level tasks such as inspection, exploration, tracking grasping, and collision-free motion planning. For both applications, we use the Minnesota Robotic Visual Tracker (a single visual sensor mounted on the end-effector of a robotic manipulator combined with a real-time vision system) to automatically select feature points on surfaces, to derive an estimate of the environmental parameter in question, and to apply a control vector based upon these estimates to guide the manipulator. The paper concludes with applications of these techniques to transportation problems such as vehicle tracking.< >
Most early research in robotic visual tracking, especially prior to 1990, separated the vision processing and robot control aspects of the system. Attempts to solve the problem close the control loop by incorporating ...
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Most early research in robotic visual tracking, especially prior to 1990, separated the vision processing and robot control aspects of the system. Attempts to solve the problem close the control loop by incorporating the output of the vision processing as an input to the control subsystem. The controlled active vision framework describes one such approach wherein dynamic target, camera, and environmental factors are incorporated via adaptive controllers that utilize the sum-of-squared differences (SSD) optical flow measurements as an input to the control loop. This paper describes work at the University of Minnesota's Artificial Intelligence, robotics, and visionlaboratory in developing the Minnesota Robotic Visual Tracker (MRVT), a controlled active vision robotic testbed. In addition, enhancements to the basic SSD algorithm are presented that produce order-of-magnitude improvements over previously reported results.< >
The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved. These situations vary from maintaining a safe distance behind a leadi...
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The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved. These situations vary from maintaining a safe distance behind a leading vehicle to safely allowing a pedestrian to cross a busy street. Environmental sensing plays a critical role in virtually all of these situations. Of the sensors available, vision sensors provide information that is richer and more complete than other sensors, making them a logical choice for a multisensor transportation system. In this paper we present robust techniques for intelligent vehicle and highway applications where computervision plays a crucial role. In particular, we demonstrate that the controlled active vision framework can be utilized to provide a visual sensing modality to a traffic advisory system in order to increase the overall safety margin in a variety of common traffic situations. We have selected two application examples, vehicle tracking and pedestrian tracking, to demonstrate that the framework can provide precisely the type of information required to effectively manage the given situation.< >
We present robust techniques for the derivation of depth from feature points on a target surface and for the accurate and high-speed tracking of moving targets. We use these techniques in a system that operates with l...
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We present robust techniques for the derivation of depth from feature points on a target surface and for the accurate and high-speed tracking of moving targets. We use these techniques in a system that operates with little or no a priori knowledge of the object-related parameters present in the environment. The system is designed under the controlled active vision framework and robustly determines parameters such as velocity for tracking moving objects and depth maps of objects with unknown depths and surface structure. Such determination of extrinsic environmental parameters is essential for performing higher level tasks such as inspection, exploration, tracking, grasping, and collision-free motion planning. For both applications, we use the Minnesota Robotic Visual Tracker (a visual sensor mounted on the end-effector of a robotic manipulator combined with a real-time vision system) to automatically select feature points on surfaces, to derive an estimate of the environmental parameter in question, and to supply a control vector based upon these estimates to guide the manipulator.< >
The conventional least squared distance method of fitting a model to a set of data points gives unreliable results when the amount of noise in the input is significant compared with the amount of data correlated to th...
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The conventional least squared distance method of fitting a model to a set of data points gives unreliable results when the amount of noise in the input is significant compared with the amount of data correlated to the model itself. The theory of robust statistics formally addresses these problems and is used in this work to develop a method of separation of the data of interest from noise. It is based on iteratively reweighted least squares algorithm where Hampel redescending function is applied for weighting data. The method has been efficiently tested in modeling synthetic and real 2D image data with second order curves.< >
This paper investigates manipulation tasks with arrays of microelectromechanical structures (MEMS). We develop a geometric model for the mechanics of microactuators and a theory of sensorless, parallel manipulation, a...
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This paper investigates manipulation tasks with arrays of microelectromechanical structures (MEMS). We develop a geometric model for the mechanics of microactuators and a theory of sensorless, parallel manipulation, and we describe efficient algorithms for their evaluation. The theory of limit surfaces offers a purely geometric characterization of microscale contacts between actuator and moving object, which can be used to efficiently predict the motion of the object on an actuator array. It is shown how simple actuator control strategies can be used to uniquely align a part up to symmetry without sensor feedback. This theory is applicable to a wide range of microactuator arrays. Our actuators are oscillating structures of single-crystal silicon fabricated in a IC-compatible process. Calculations show that these actuators are strong enough to levitate and move, for example, a piece of paper.< >
The computation of 3-D structure from motion using a monocular sequence of images in the paradigm of active vision is investigated in this paper. Robotic tasks such as navigation, manipulation, and object recognition ...
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Binocular stereo, vergence stereo, and depth from focus have been extensively studied in isolation in the past for extraction of depth. These passive approaches have there oivn strengths and limitations. All these cue...
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A Simulation, Animation, Visualization and Interactive Control (SAVIC) environment has been developed for the design and operation of an integrated robotic manipulator system. This unique system possesses the abilitie...
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A Simulation, Animation, Visualization and Interactive Control (SAVIC) environment has been developed for the design and operation of an integrated robotic manipulator system. This unique system possesses the abilities for (1) multi-sensor simulation, (2) kinematics and locomotion animation, (3) dynamic motion and manipulation animation, (4) transformation between real and virtual modes within the same graphics system, (5) ease in exchanging software modules and hardware devices between real and virtual world operations, and (6) interfacing with a real robotic system. This research is focused on enhancing the overall productivity of an integrated human-robot system. This paper describes a working system and illustrates the concepts by presenting the simulation, animation and control methodologies for a unique mobile robot with articulated tracks, a manipulator, and sensory modules.
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