Robot Motion Information Research Articles

Symmetric Kullback-Leibler Metric Based Tracking Behaviors for Bioinspired Robotic Eyes.

A symmetric Kullback-Leibler metric based tracking system, capable of tracking moving targets, is presented for a bionic spherical parallel mechanism to minimize a tracking error function to simulate smooth pursuit of human eyes. More specifically, we propose a real-time moving target tracking algorithm which utilizes spatial histograms taking into account symmetric Kullback-Leibler metric. In the proposed algorithm, the key spatial histograms are extracted and taken into particle filtering framework. Once the target is identified, an image-based control scheme is implemented to drive bionic spherical parallel mechanism such that the identified target is to be tracked at the center of the captured images. Meanwhile, the robot motion information is fed forward to develop an adaptive smooth tracking controller inspired by the Vestibuloocular Reflex mechanism. The proposed tracking system is designed to make the robot track dynamic objects when the robot travels through transmittable terrains, especially bumpy environment. To perform bumpy-resist capability under the condition of violent attitude variation when the robot works in the bumpy environment mentioned, experimental results demonstrate the effectiveness and robustness of our bioinspired tracking system using bionic spherical parallel mechanism inspired by head-eye coordination.

Pan/Tilt Camera Control for Vision Tracking System Based on the Robot Motion and Vision Information

AbstractThis paper presents a vision tracking system, based on the robot motion and vision information. For mobile robots, it is difficult to collect continuous vision information while they are in motion due to the unstable vision information. To solve this problem, the proposed vision tracking system estimates the robot position relative to a target and rotates a camera towards the target based on the estimated position information. This concept is derived from the human eye reflex mechanisms, known as the Vestibulo-Ocular Reflex (VOR) and the Opto-Kinetic Reflex (OKR). In the proposed vision tracking system, the VOR concept for compensating the head motion is realized by the feedforward control using the robot motion information from a 3-axis gyroscope and wheel encoders. To realize the OKR concept, targeting errors are periodically compensated by using the vision feedback information. An actuation module consists of pan/tilt motion motors and the camera which performs the pan and tilt functions to locate a target at the center of an image plane. The proposed vision tracking system is integrated with a two-wheeled robot. The performance of the proposed system is evaluated by extensive experiments. The proposed system shows a significant improvement in tracking performance with small targeting error. Also, the necessities of the position feedforward and vision feedback controls are verified by the experiments for the illumination change condition and the static tilting motion.

Mobile Robot Vision Tracking System Using Dead Reckoning & Active Beacons

AbstractThis paper presents a new vision tracking system for mobile robot by integrating information received from encoders, inertial sensors, and active beacons. The proposed system accurately determines mobile robot position and orientation using relative and absolute position estimates, and rotates the camera towards the target during locomotion. Among the implemented sensors, the encoder data give relatively accurate robot motion information except when wheels slip. On the other hand inertial sensors have the problem of integration of noisy data, while active beacons are slow when compared to other sensors. The designed system compensates the sensors limitations and slip error by switching between two Kalman filters, built for slip and no-slip cases. Each Kalman filter uses different sensors combination and estimates robot motion respectively. The slip detector is used to detect the slip condition by comparing the data from the accelerometer and encoder to select the either Kalman filter as the output of the system. Based on the proposed sensor fusion method, a vision tracking system is implemented on a two-wheeled robot. The experimental results depict that proposed system is able to locate robot position with significantly reduced position errors and successful tracking of the target for various environments and robot motion scenarios.

Industrial robotic system programmed from CAD files – an update

PurposeIn this paper a solution capable of extracting robot motion information from CAD data is briefly presented and explored.Design/methodology/approachThe motion information is added by the user to the CAD file, defining in this way the approach, fly‐by and welding trajectories. So the user programs the robot. An application is then used to extract that information and constitute a pre‐program, which should be tuned with the real robot using a small set of rules. In this way, versatility of robotic cells is improved and its programming is simplified.FindingsThe developed CAD interface is tested using a robot welding experiment on a steel beam used for industrial buildings.Originality/valueThis paper builds on work previously described in “CAD interface for automatic robot welding programming” published in Industrial Robot, Vol. 31, No. 1.

CAD interface for automatic robot welding programming

Industrial robots play an important role in industry, due to their flexibility. Many applications (almost all that require human intervention) may be performed with advantages by robots. Nevertheless, set‐up operations, necessary when changing production models, are still tricky and time‐consuming. It is common to have detailed data of working pieces in computer aided design (CAD) files, resulting from product design and project. This information is not used satisfactorily, or even not used at all, for robot programming. In this paper, we propose a solution capable of extracting robot motion information from the CAD data.

センサベーストロボットシステムのための運動情報マネージメント 運動情報に基づく作業点誤差モデリングと倣いロボット診断への応用

Integrated motion information management for use in highly accurate sensor-based robot systems that increases the precision and quality of robotic tasks is described. In this method, the sources of the recognition errors are easily identified by introducing the error-model using the information obtained by the proximity sensor as well as the information about the robot motion acquired by commanded simple testing motions, and these identified error-sources are used to reduce the recogniton errors. Motion reliability is determined based on the robot-motion information, and this reliability is used to reduce the influence of the recognition errors to the whole sensor-based robot system. As a result of this tuning the sensor-based robot system is set up to be close to optimal as possible. Experimental results from a typical visual path-tracking task showed that the tracking error is significantly reduced by using this motion information management system.

A three dimensional localiser for autonomous robot vehicles

SummaryA novel design of a three dimensional localiser intended for autonomous robot vehicles is presented. A prototype is implemented in air using ultrasonic beacons at known positions, and can be adapted to underwater environments where it has important applications, such as deep sea maintenance, data collection and reconnaissance tasks. The paper presents the hardware design, algorithms for position and orientation determination (six degrees of freedom), and performance results of a laboratory prototype. Two approaches are discussed for position and orientation determination – (i) fast single measurement set techniques and (ii) computationally slower Kalman filter based techniques. The Kalman filter approach allows the incorporation of robot motion information, more accurate beacon modelling and the capability of processing data from more than four beacons, the minimum number required for localisation.