Tracking Compensation of a Moving Target for a Biped Robot Based on Vision Sensor

2010 ◽  
Vol 44-47 ◽  
pp. 788-793
Author(s):  
Yun De Shen ◽  
Dong Soo Cho ◽  
Chang Doo Kee ◽  
Zhen Zhe Li
Keyword(s):  
Rotation Angle ◽  
Biped Robot ◽  
Position Estimation ◽  
Moving Target ◽  
Vision Sensor ◽  
Matching Algorithm ◽  
Image Frame ◽  
Affine Transform ◽  
Search Area ◽  
Starting Point

In this paper, the visual tracking algorithm for a moving target is proposed for the biped robot of which camera movement is irregular. Hexagonal Matching Algorithm is used to measure the changes of size, location, and rotation angle for a moving object from its image frame. For enhancing the efficiency of the tracking, we can adaptively adjust the starting point and the size of search area from the image information obtained. Finally, by using Affine Transform and Kalman Filter, the position estimation of the moving target is refined against the swing of the camera. Experiments with 20-DOF biped robot using mono vision sensor are implemented to prove the reliability of the proposed method.

scholarly journals Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1483 ◽  
Author(s):  
Manuel Vega-Heredia ◽  
Ilyas Muhammad ◽  
Sriharsha Ghanta ◽  
Vengadesh Ayyalusami ◽  
Siti Aisyah ◽  
...  
Keyword(s):  
Orientation Angle ◽  
Position Estimation ◽  
Measurement Unit ◽  
Vision Sensor ◽  
Window Frame ◽  
Cleaning Robot ◽  
Heading Angle ◽  
Glass Facade ◽  
Cleaning Robots ◽  
Glass Façade

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

scholarly journals Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4179 ◽  
Author(s):  
Stelian Dolha ◽  
Paul Negirla ◽  
Florin Alexa ◽  
Ioan Silea
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Data Access ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Node Localization ◽  
Received Signal Strength Indicator ◽  
Starting Point

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

An Improved Geomagnetic Matching Algorithm Using Rotation Angle Search Technique

2011 ◽  
Vol 383-390 ◽  
pp. 7576-7581 ◽  
Author(s):  
Ya Jie Liu ◽  
Yan Zhao ◽  
Fa Lin Wu
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Rotation Angle ◽  
Inertial Navigation ◽  
Search Technique ◽  
Matching Accuracy ◽  
Search Range ◽  
Matching Algorithm ◽  
Contour Matching ◽  
Matching Probability

The accumulation course angle error of inertial navigation system will decrease the accuracy and reliability of an geomagnetism aided inertial navigation system using a geomagnetic contour matching algorithm. To improve the matching accuracy, the matching track and true track should be as parallel as possible. An improved geomagnetic matching algorithm is presented by introducing rotation angle search technique. To reduce the computation burden, improve operation efficiency and reduce false matching probability, a new search area determination method is proposed, which redefines the search region and reduces the search range. Simulation results demonstrate the effectiveness of the proposed algorithm and the improvement in the matching accuracy.

scholarly journals Method for considering angle error in the position estimation of a moving target using ultrasonic array

2021 ◽  
Author(s):  
Masamichi Hattori ◽  
Asuka Tsujii ◽  
Takashi Kasashima ◽  
Hiroyuki Hatano ◽  
Takaya Yamazato
Keyword(s):  
Position Estimation ◽  
Moving Target ◽  
Angle Error ◽  
Ultrasonic Array
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