Abstract:
Vision-based Autonomous Robots field is becoming rapidly popular, according to the
artificial intelligence revolution. The proposed system is a composed Unmanned
Ground Vehicle vision-based target tracking robot prototype. Target tracking is useful
in multiple real-life issues such as the assistance and security fields. Acquitted visual
input processing is applied through using OpenCV library. The object detection stage
of UGV system, is done based on a pre-built deep learning object detection model.
YOLOv3-tiny is used to detect objects, which is a light computation-cost version
comparing to original YOLO models, and the other complex deep-learning networks.
Object to track is specified to be a human only. The target tracking algorithm is based
on a sequence of mathematical equations with Region of Interest and stream’s frame
coefficients. Coefficients refer to the values of locations according to x-axis and y-axis
of the frame. A simple mathematical technique is used for the delayed feedback issue.
The locomotion of UGV is based on transmitted commands from algorithm to the
motors through local network connection. Ultra-sound technique is used for collision
avoidance. Robotic eye-based face tracking is a subsystem. In the subsystem, the face
detection stage in Robotic eye-based face tracking subsystem is done using Harr-like
cascade. The locomotion algorithm is based on various trigonometry rules using
pan/tilt and servos means. Innovative methods are followed for performance
measurement and comparison. The results show, an autonomous behavior, streamlined
and accurate locomotion of tracking.
Keywords: Autonomous Robot, UGV, Object Tracking, Target Tracking, Human
Following, Object Detection, YOLOv3, COCO dataset, Face tracking, Robotic eye,
pan/tilt
