Search

Pan-tilt-zoom (PTZ) cameras have been widely used in recent years for monitoring and surveillance applications. These cameras provide flexible view selection as well as a wider observation range. This makes them suitable for vision-based traffic monitoring and enforcement systems. To employ PTZ cameras for image measurement applications, one first needs to calibrate the camera to obtain meaningful results. For instance, the accuracy of estimating vehicle speed depends on the accuracy of camera calibration and that of vehicle tracking results. This paper presents a novel calibration method for a PTZ camera overlooking a traffic scene. The proposed approach requires no manual operation to select the positions of special features. It automatically uses a set of parallel lane markings and the lane width to compute the camera parameters, namely, focal length, tilt angle, and pan angle. Image processing procedures have been developed for automatically finding parallel lane markings. Interesting experimental results are presented to validate the robustness and accuracy of the proposed method. Index Terms--Background segmentation, camera calibration, image measurement, image processing, lane-marking detection, traffic monitoring.

The problem of navigating a mobile robot among moving obstacles is usually solved on the condition of knowing the velocity of obstacles. However, it is difficult to provide such information to a robot in real time. In this paper, we present an environment predictor that provides an estimate of future environment configuration by fusing multisensor data in real time. The predictor is implemented by an artificial neural network (ANN) trained using a relative-error-backpropagation (REBP) algorithm. The REBP algorithm enables the ANN to provide output data with a minimum relative error, which is better than conventional backpropagation (BP) algorithms in this prediction application. The mobile robot can, therefore, respond to anticipated changes in the environment. The performance is verified by prediction simulation and navigation experiments. Index Terms - Artificial neural networks, environment prediction, mobile robots, moving obstacles, training algorithm.

To move efficiently in an unknown or uncertain environment, a mobile robot must use observations taken by various sensors to construct information for path planning and execution. A reasonably accurate representation of the external world would also be very useful for robot self-localization. One of the merits of applying multiple sensors to a mobile robot is the enhancement of environment recognition. In this paper, we propose to use sensory information combined from double ultrasonic sensors and a CCD camera. We developed an algorithm based on a dual-transducer design to eliminate errors resulting from the beam opening angle of ultrasonic sensors. An extended discrete Kalman filter (EDKF) was designed to fuse raw sensory data and to reduce the influence of specular reflection of ultrasonic type transducers, thereby providing a more reliable representation for environment perception. Computer simulation, as well as practical experimental results demonstrate that this sensory system can provide useful and comprehensive environment perception for intelligent robotics.

This study presents a motion planning method for coordinating multiple mobile robots for collision‐free navigation. A control architecture that uses cloud computing is proposed for the acquisition of real‐time robotic data and to coordinate multiple robots in an unstructured environment. The system consists of collision‐free path planning for multiple robots, obstacle avoidance, and navigation control. The path planning method was developed based on the optimal reciprocal collision avoidance (ORCA) algorithm to generate a velocity set of each robot in the system. A laser scanner is used to detect obstacles for each robot. Appropriate obstacle avoidance behaviors are generated using sensory information. A behavior‐fusion control scheme combines obstacle avoidance and collision‐free path planning to coordinate multiple robots. The proposed anti‐collision motion planning method for multiple robots prevents collisions with unexpected obstacles and with other robots in the environment. The experimental results show that multiple mobile robots can navigate to targets that are assigned by the cloud server without colliding with other robots, regardless of whether there are unexpected static or dynamic obstacles in the environment. [ABSTRACT FROM AUTHOR]

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic rangefinders are combined to predict obstacle positions at next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual-force-based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.