Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system consists of three sub-modules: monocular camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor fusion. An improved visual tracking algorithm is presented to provide partial location estimation (2-D). The algorithm is designed to overcome severe occlusions, scale variation, target missing and achieve robust re-detection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D). EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the superior performance of the 3-D tracking system in terms of both the accuracy and robustness.
In the intelligent transportation system, various accident avoidance techniques have been applied. Among them, one of the most common issues is the collision, which is yet unsolved problem. To this end, we develop collision warning and avoidance system (CWAS), which is implemented in the wheeled mobile robot. Likewise, path planning is a crucial problem in the mobile robots to perform a given task correctly. Here, a tracking system is presented for the mobile robot, which follows an object. Thus, we have implemented an integrated CWAS and tracking system in the mobile robot. Both systems can be activated independently. In the CWAS, the robot is controlled through a remotely controlled device, and collision prediction and avoidance functions are performed. In the tracking system, the robot performs tasks autonomously, where the robot maintains a constant distance from the followed object. The surrounding information is obtained through the range sensors, and the control functions are performed through the microcontroller. The front, left, and right sensors are activated to track the object, and all the sensors are used for the CWAS. Two algorithms based on the sensory information are developed with the distance control approach. The proposed system is tested using the binary logic controller and the fuzzy logic controller (FLC). The comparison of both controllers is also described by preferring time delay and complexity. The efficiency of the robot is improved by increasing smoothness in motion using the FLC, achieving accuracy in tracking, and advancements in the CWAS. Finally, simulation and experimental outcomes have displayed the authenticity of the system. A blind spot detection device for protection against misshapenness such as automobiles collisions, obstacles, and accident that leads to great loss of human lives and can have disastrous results.Technology used for this purpose worked by detecting the other automobiles, obstacles and bystanders. Upon detecting, the device triggers a timer that delays the activation of alarm circuitry for a brief period of time.This time delay is instituted to minimize the triggers of nuisance alarm by a momentary intrusion in the hazard zone. If the obstaclepsilas presence is still detected after the delay time, LED's and audible alarms are triggered to alert the system operator of the dangerous situation. The alarms remain activated for a time period, allowing the operator to clear the hazard zone. An non-linear Bayesian regression engine for robotic tracking based on an ultrasonic RF sensor unit is presented in this paper. The proposed system is able to maintain systematic tracking of a leading human in indoor outdoor settings with minimalistic instrumentation. Compared to popular camera based localization system the sonar array RF based system has the advantage of being insensitive to background light intensity changes, a primary concern in outdoor environments. In contrast to single-plane laser range finder based tracking the proposed scheme is able to better adapt to small terrain variations, while at the same time being a significantly more affordable proposition for tracking with a robotic unit. A key novelty in this work is the utilisation of Gaussian Process Regression (GPR) to build a model for the sensor unit, which is shown to compare favourably against traditional linear triangulation approaches. The covariance function yield by the GPR sensor model also provides the additional benefit of outlier rejection. We present experimental results of indoors and outdoors tracking by mounting the sensor unit on a Garden Utility Transportation System (GUTS) robot and compare the proposed approach with linear triangulation which clearly show the inference engine capability to generalise relative localisation of human and a marked improvement in tracking accuracy and robustness.