GPS satellite (4) ranging signals (6) received (32) on comm1, and DGPS auxiliary range correction signals and pseudolite carrier phase ambiguity resolution signals (8) from a fixed known earth base station (10) received (34) on comm2, at one of a plurality of vehicles/aircraft/automobiles (2) are computer processed (36) to continuously determine the one's kinematic tracking position on a pathway (14) with centimeter accuracy. That GPS-based position is communicated with selected other status information to each other one of the plurality of vehicles (2), to the one station (10), and/or to one of a plurality of control centers (16), and the one vehicle receives therefrom each of the others' status information and kinematic tracking position. Objects (22) are detected from all directions (300) by multiple supplemental mechanisms, e.g., video (54), radar/lidar (56), laser and optical scanners. Data and information are computer processed and analyzed (50,52,200,452) in neural networks (132, FIGS. 6-8) in the one vehicle to identify, rank, and evaluate collision hazards/objects, an expert operating response to which is determined in a fuzzy logic associative memory (484) which generates control signals which actuate a plurality of control systems of the one vehicle in a coordinated manner to maneuver it laterally and longitudinally to avoid each collision hazard, or, for motor vehicles, when a collision is unavoidable, to minimize injury or damage therefrom. The operator is warned by a heads up display and other modes and may override. An automotive auto-pilot mode is provided.

GPS vehicle collision avoidance warning and control system and method

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

https://cvrr.ucsd.edu/publications/2013/SayananTrivedi_IEEETITS2013_VehicleSurvey.pdf

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

The subject of the paper is the motion control problem of wheeled mobile robots (WMRs) in environments without obstacles. With reference to the popular unicycle kinematics, it is shown that dynamic feedback linearization is an efficient design tool leading to a solution simultaneously valid for both trajectory tracking and setpoint regulation problems. The implementation of this approach on the laboratory prototype SuperMARIO, a two-wheel differentially driven mobile robot, is described in detail. To assess the quality of the proposed controller, we compare its performance with that of several existing control techniques in a number of experiments. The obtained results provide useful guidelines for WMR control designers.

/pdf/wmr-control-via-dynamic-feedback-linearization-design-25vbjh5hnl.pdf

WMR control via dynamic feedback linearization: design, implementation, and experimental validation

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.

/pdf/machine-learning-methods-for-classifying-human-physical-b62qw3cvfj.pdf

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

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 LaSalle's 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.

https://ir.nctu.edu.tw:443/bitstream/11536/29781/1/000167656200010.pdf

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

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

Speech recognition is common in electronic appliances and personal services, but its use for industrial and medical purposes is rare because of the presence of motion ambiguity. For minimally invasive surgical robotic assistants, this ambiguity arises because the robotic motion is not calibrated to the camera images. This paper presents a design for a speech recognition interface for an HIWIN robotic endoscope holder. A new intentional speech control is proposed to control movement over long distances. To decrease ambiguity, a method is proposed for voice-to-motion calibration that compares the degree of change in the endoscope image for a voice command. A speech recognition algorithm is implemented on Ubuntu OS, using CMU Sphinx. The control signal is sent to the robot controller using serial-port communication through a RS232 cable. The experimental results show that the proposed intentional speech control strategy has a navigation precision of up to 3.1° of angular displacement for the endoscope. The overall system processing time, including robotic motion, is 3.22 s for ∼1.8-s speech duration. The reference image navigation range is from 2.5 mm for ∼0.5-s speech duration up to 6 mm for ∼1.8-s speech duration, using a setup with camera tip that is located at a distance of 5 cm from the remote center of motion point.

A Study on Speech Recognition Control for a Surgical Robot

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

https://ir.nctu.edu.tw:443/bitstream/11536/11714/1/000240756700010.pdf

Dynamic Calibration of Pan&#8211;Tilt&#8211;Zoom Cameras for Traffic Monitoring

This paper presents a method of mood transition design of a robot for autonomous emotional interaction with humans. A 2-D emotional model is proposed to combine robot emotion, mood, and personality in order to generate emotional expressions. In this design, the robot personality is programmed by adjusting the factors of the five factor model proposed by psychologists. From Big Five personality traits, the influence factors of robot mood transition are determined. Furthermore, a method to fuse basic robotic emotional behaviors is proposed in order to manifest robotic emotional states via continuous facial expressions. An artificial face on a screen is a way to provide a robot with a humanlike appearance, which might be useful for human-robot interaction. An artificial face simulator has been implemented to show the effectiveness of the proposed methods. Questionnaire surveys have been carried out to evaluate the effectiveness of the proposed method by observing robotic responses to a user's emotional expressions. Preliminary experimental results on a robotic head show that the proposed mood state transition scheme appropriately responds to a user's emotional changes in a continuous manner.

/pdf/robotic-emotional-expression-generation-based-on-mood-2rdxefom7j.pdf

Kai-Tai Song

Papers

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

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

A Study on Speech Recognition Control for a Surgical Robot

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

Robotic Emotional Expression Generation Based on Mood Transition and Personality Model

Kai-Tai Song

Papers

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

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

A Study on Speech Recognition Control for a Surgical Robot

Dynamic Calibration of Pan&#8211;Tilt&#8211;Zoom Cameras for Traffic Monitoring

Robotic Emotional Expression Generation Based on Mood Transition and Personality Model

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