Stereo Vision based Object Tracking Control for a Movable Robot Head

Abstract

In this paper, we have developed a visual tracking control system on a purposely built movable robot head mounted on a Baxter robot. The tracking control algorithm used a stereo vision system provided by a Bumblebee camera, facilitated by the MATLAB computer vision toolbox. The objective of this work is to enable a robot to move its head as in a similar manner as our humans beings to focus vision on a moving object. Identification of object and estimation of its coordinate are performed based on image processing techniques. A fuzzy logic technique is applied to control the moving head in order to bring human like motion into the robot. The method developed in this work can be extended to any other movable robot head platform. Extensive experimental studies have been performed to test the effectiveness and efficiency of the proposed method.

Obstacle detection for autonomous systems using stereoscopic images and bacterial behaviour

“…Depending on the application it is possible to use different kinds of sensors, but those capable of providing visual information are the ones that provide more relevant information [9]. In this sense, systems with two cameras turn out to be more advantageous than systems with a single camera [8], since they provide information on the depth and orientation of the obstacle [4,[10][11][12]. Digital cameras as fundamental elements of optical sensors have been used extensively for the robotic arm motion control solution.…”

“…This strategy is known as Visual Servoing or Vision-Based Robot Control (VS) and is characterized by having as feedback information the image of a camera [13]. The aim is to support the robot's decision making with eyes that take optical information from its own perspective and in parallel (separated by a certain distance) [11]. The distance between the robot and the obstacle can be determined depending on the distance between the obstacle positions in both images, and the focal distance of the cameras [14].…”

“…In addition, the generation of 3D models is highly dependent on the quality of two-dimensional images, which are strongly affected by lighting conditions [18]. The computation of the distance to the obstacle takes into account the angular distance, the distance between cameras and the pixels of the images [7,11]. However, in many applications, it is not necessary to rebuild the entire environment, which considerably reduces the computational requirement [19].…”

“…There are two strategies for estimating the distance to the obstacle in stereoscopic vision: active method and passive method [10,22]. In the first case, the sensor system sends signals to the obstacle such as visible light or laser signals, which are then detected and analyzed [11]. The ability of these sensors to establish distances is superior to human vision, but they are also costly and complex to implement, and they have unresolved problems.…”

An Automated Robot-Car Control System with Hand-Gestures and Mobile Application Using Arduino

“…Thus, in previous literature, most of them are line-sensing robots [51][52][53] that are controlled with Infrared LEDs, HF band short-range RFID systems [54] and CNY 70 sensor [55]. In addition, vision-based technology [56,57] are also used to control the movement of the robot. Furthermore, the automated robot control system with six sense [58][59][60], and ZigBee based system [61][62][63][64] have also been implemented.…”