Kai-Tai Song

Tracking control of unicycle-modeled mobile robots using a saturation feedback controller

The tracking control problem with saturation constraint for a class of unicycle-modeled mobile robots is formulated and solved using the backstepping technique and the idea from the LaSalles invariance principle. A global result is presented in which several constraints on the linear and the angular velocities of the mobile robot from recent literature are dropped. The proposed controller can simultaneously solve both the tracking and regulation problems of a unicycle-modeled mobile robot. With the proposed control laws, the robot can globally follow any path specified by a straight line, a circle or a path approaching the origin using a single controller. As demonstrated, the circular and parallel parking control problem are solved using the proposed controller. Computer simulations are presented which confirm the effectiveness of the proposed tracking control law. Practical experimental results validate the simulations.

Real-time image tracking for automatic traffic monitoring and enforcement applications

Abstract This paper presents an image tracking system and its applications for traffic monitoring and accident detection at road intersections. Locations of motorcycles as well as automobiles are obtained in real time using the active contour model approach. Image measurement is further incorporated with Kalman filtering techniques to track individual vehicle motion. To initialize image tracking of vehicles at a junction, we propose a contour initialization method based on the concept of contour growing. Using a specially designed circuit board, a stand-alone image tracker has been designed and created for automatic traffic monitoring. We successfully achieved real-time image tracking of multi-lane vehicles. Interesting experimental results are presented to demonstrate the effectiveness of the proposed system.

Fuzzy Navigation Of A Mobile Robot

A navigation system based on fuzzy logic controllers is developed for a mobile robot in an unknown environment. The structure of this fuzzy navigation system features the com- bination of sensor system, fuzzy controllers for motion planning and the motion control system for real-time execution. Six ultrasonic sensors on-board the mobile robot are used for distance measurement to the immediate obstacles. Sensor data are fuzzified to be the inputs of the fuzzy controller. Three states, each with five quantized levels are used to define the fuzzy set. Two fuzzy controllers are designed to handle the naviga- tion problem. Each fuzzy controller, which cor- responds to the turn right or turn left condition, has four inputs, two outputs and 81 rules. The outputs are the command velocities to the left and right wheels, which drive the mobile robot. These command velocities are sent to the lower level motion control system. The performance of this navigation system is tested by computer sim- ulation. Satisfactory results have been obtained and are presented in this paper.

Dynamic Calibration of Pan–Tilt–Zoom Cameras for Traffic Monitoring

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

Heuristic fuzzy-neuro network and its application to reactive navigation of a mobile robot

Abstract A novel pattern recognition approach to reactive navigation of a mobile robot is presented in this paper. A heuristic fuzzy-neuro network is developed for pattern-mapping between quantized ultrasonic sensory data and velocity commands to the robot. The design goal was to enable an autonomous mobile robot to navigate safely and efficiently to a target position in a previously unknown environment. Useful heuristic rules were combined with the fuzzy Kohonen clustering network (FKCN) to build the desired mapping between perception and motion. This method provides much faster response to unexpected events and is less sensitive to sensor misreading than conventional approaches. It allows continuous, fast motion of the mobile robot without any need to stop for obstacles. The effectiveness of the proposed method is demonstrated in a series of practical tests on our experimental mobile robot.

Reactive navigation in dynamic environment using a multisensor predictor

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 range finders are combined to predict obstacle positions at the 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.

Heterogeneity-Projection Hard-Decision Color Interpolation Using Spectral-Spatial Correlation

This paper presents a novel heterogeneity-projection hard-decision (HPHD) color interpolation procedure for reproduction of Bayer mosaic images. The proposed algorithm aims to estimate the optimal interpolation direction and perform hard-decision interpolation, in which each pixel only needs to be interpolated once. A new heterogeneity-projection scheme based on a novel spectral-spatial correlation concept is proposed to estimate the best interpolation direction directly from the original mosaic image. Using the proposed heterogeneity-projection scheme, a hard-decision rule can be decided before performing the interpolation. The advantage of this scheme is that it provides an efficient way for decision-based algorithms to generate improved results using fewer computations. Compared with three recently reported demosaicing techniques, Gunturks, Lus, and Lis methods, the proposed HPHD outperforms all of them in both PSNR values and S-CIELAB DeltaEab * measures by utilizing 25 natural images from Kodak PhotoCD

Environment prediction for a mobile robot in a dynamic environment

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.

Design and experimental study of an ultrasonic sensor system for lateral collision avoidance at low speeds

In this study, we-design and implement an ultrasonic sensor system for lateral collision avoidance of vehicles at low speeds. The developed sensor system is useful for detecting vehicles, motorcycles, bicycles and pedestrians that pass by the lateral side of a vehicle. The system can be adopted to enhance the rear-view mirrors of present vehicles, which have blind spots on the lateral sides. Ultrasonic sensors, which have been widely used on cars for rear object detection during parking, are developed for lateral object detection at low speeds. Detailed experimental studies are presented in this paper. Experimental results show that the proposed system can detect a vehicle at speeds up to 40 km/hr with a maximum range of 6 meters. Moreover, the influence of wind on the measurement is also investigated. The developed sensor system gives satisfactory results for a wind speed up to 35 km/hr.

Image-Based Traffic Monitoring With Shadow Suppression

For a vision-based traffic monitoring and enforcement system, shadows of moving objects often cause serious errors in image analysis due to misclassification of shadows and moving vehicles. An effective shadow suppression method is thus required to improve the accuracy of image analysis and this paper proposes a novel color-space ratio model for detecting shadow pixels in traffic imagery. The proposed approach does not require many image sequences for constructing the model. Instead the model can be easily built up using a shadow region in a single image frame. To increase the accuracy of shadow detection, we design two types of spatial analysis to verify actual shadow pixels. Comparative results show that the proposed method works better than several well-known methods. The proposed methods have been applied to an image-based traffic monitoring system for detecting shadow pixels in traffic imagery. The experimental results not only validate the feasibility of the proposed algorithm but also successfully estimate traffic parameters such as traffic flows, traffic densities, vehicle turn ratios and vehicle speeds, all with satisfactory accuracy