Autonomous bicycles offer numerous potentials for smart city applications thanks in part to their light weight, safe autonomy, being optionally manned, and last-mile delivery. This paper describes the design of a self-stabilizing autonomous bicycle with electric linear actuators. The high-speed linear actuator is mounted between the seat and the handlebar of the autonomous bicycle, which provides the bicycle with high peak power and energy efficiency. Physical tests are carried out to verify automatic steering and speed regulation capabilities of the autonomous bicycle.
Riderless bicycles, which belong to the class of narrow autonomous vehicles, offer numerous potentials to improve living conditions in the smart cities of the future. Various obstacles exist in achieving full autonomy for this class of autonomous vehicles. One of these significant challenges lie within the synthesis of automatic control algorithms that provide self-balancing and maneuvering capabilities for this class of autonomous vehicles. Indeed, the nonlinear, underactuated, and non-minimum phase dynamics of riderless bicycles offer rich challenges for automatic control of these autonomous vehicles. In this paper, we report on implementing linear parameter varying (LPV)-based controllers for balancing our constructed autonomous bicycle, which is equipped with linear electric actuators for automatic steering, in the upright position. Experimental results demonstrate the effectiveness of the proposed control strategy.
In this paper, we present results obtained through simulation of connected-autonomous semi-trucks that are operating in a leader-follower configuration. Autonomy is enabled in this configuration with a very lean sensor package on each truck, precision global positioning system (GPS), radar-based automated cruise control system (ACC), and dedicated short-range vehicle-to-vehicle communication system (DSRC). Our simulation includes modeling the operating environment, namely, the high-speed test track at the American Center for Mobility (ACM); the sensors, namely, GPS, ACC, and DSRC; and vehicle dynamics of semi-trucks. Simulation results in this paper are focused on measuring the safety margin of the follower semi-truck under different environmental conditions. We studied adverse weather and measured the decrease in safety margins with the increase in precipitation.
Miniature blimps will have numerous applications in future smart cities. This paper presents the design of an autonomous blimp that can be autonomously operated and controlled. In order to be able to operate over long periods of time, the blimp design employs a novel actuation mechanism with only one servomotor and two DC motors. Experiments are carried out to demonstrate the capabilities of the constructed autonomous blimp.
Unmanned ground vehicle (UGV) technology can be used in a number of ways to assist in counter-terrorism activities. Robots can be employed for a host of terrorism deterrence and detection applications. As reported in last year's Aerosense conference, the U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) and Utah State University (USU) have developed a
tele-operated robot called ODIS (Omnidirectional Inspection System) that is particularly effective in performing under-vehicle inspections at security checkpoints. ODIS' continuing development for this task is heavily influenced by feedback received from soldiers and civilian law enforcement personnel using ODIS-prototypes in an operational environment. Our goal is to convince civilian law enforcement and military police to replace the traditional "mirror on a stick" system of looking under cars for bombs and contraband with ODIS. This paper reports our efforts in the past one year in terms of optimizing ODIS for the visual inspection task. Of particular concern is the design of the vision system. This paper documents details on the various issues relating to ODIS' vision system - sensor, lighting, image processing, and display.
Experiments with the LOIS (Likelihood Of Image Shape) Lane detector have demonstrated that the use of a deformable template approach allows robust detection of lane boundaries in visual images. The same algorithm has been applied to detect pavement edges in millimeter wave radar images. In addition to ground vehicle applications involving lane sensing, the algorithm is applicable to airplane applications for tracking runways in either visual or radar data. Previous work on LOIS has focused on the problem of detecting lane edges in individual frames. This paper describes extensions to the LOIS algorithm which allow it to smoothly track lane edges through maneuvers such as lane changes.
The problem of determining the offset to lane markings is an important one in designing vision-based automotive safety systems that operate on structured road environments. The lane offset information is critical for lateral control of the automobile. In this paper, we investigate the use of this information for an autonomous robot's lane-keeping task. We employ a deformable template-based algorithm for determining the location of lane markings in visual images taken from a side-looking camera. The matching criteria involves a modification of the standard signal-to-noise (SNR) ratio-based matched filtering criteria. A KL-type color transformation is used for transforming the RGB channels of the given image onto a composite color channel, in order to eliminate some of the noise. The standard perspective transformation is used for transforming the offset information from image coordinates onto ground coordinates. The resulting algorithm, named STARLITE is robust to shadows, specular reflections, road cracks, etc. Experimental results are provided to illustrate the performance of STARLITE and compare its performance to the AURORA algorithm, and the SNR-based matched filter.
In a target-rich battlefield environment, a shipboard or an airborne radar must maintain situational awareness while tracking and identifying targets. Often the opportunity to dwell on each target long enough for confident identification via high resolution SAR/ISAR imaging will not exist, especially for those engagement geometries where the relative translational motion of the aircraft does not result in large rotation rates. Inadvertent aircraft tactical dither often generates enough target rotational during a brief imaging interval to allow the formation of an ISAR image with low crossrange resolution. We have developed an automated identification procedure that utilizes this resolution, along with high range resolution, to produce confident target identification. The advanced signal processing algorithms employed extract feature measurements from the complex ISAR image. including accurate measurements of the two-dimensional positions, amplitudes and range extents of the dominant target scatterers. A deformable template matching procedure is used to correlate these 'measured features' with those predicted for each candidate aircraft in a database generated from readily available diagrams, photographs and CAD models. After obtaining the optimal fit between the measured and predicted features for each candidate aircraft, the 'most likely' candidate is selected using a conventional Bayes classifier.
This paper presents a simulation and comparison of two different infrared (IR) imaging systems in terms of their use in automotive collision avoidance and vision enhancement applications. The first half of this study concerns the simulations of a `cooled' shortwave focal plane array infrared imaging system, and an `uncooled' focal plane array infrared imaging system. This is done using the United States Army's Tank-Automotive Research Development and Engineering Center's (TARDEC) thermal image model -- (TTIM). Visual images of automobiles as seen through a forward looking infrared sensor are generated, by using TTIM, under a variety of viewing range and rain conditions. The second half of the study focuses on a comparison between the two simulated sensors. This comparison is undertaken from the standpoint of the ability of a human observer to detect potential (collision) targets, when looking through the two different sensors. A measure of the target's detectability is derived for each sensor by using the TARDEC's visual model (TVM). The authors found the uncooled pyroelectric FPA to give excellent imagery and, combined with the advantages of the 7.5 - 13.5 band in the atmosphere and the higher blackbody exitance in the 7.5 - 13.5 band, the 7.5 - 13.5 uncooled sensor is therefore the better choice for imaging through numerous atmospheric conditions compared to the 3.4 - 5.5 cooled sensor.
In this paper the problem of detecting objects in the presence of clutter is studied. The images considered are obtained from both visual and infrared sensors. A feature-based segmentation approach to the object detection problem is pursued, where the features used are computed over multiple spatial orientations, and frequencies. The method proceeds as follows: A given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture `energy' is computed in a window around each transformed image pixel. The texture `energy' features, and their spatial locations, are inputted to a least squared error based clustering algorithm. This clustering algorithm yields a segmentation of the original image -- it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across the different spatial orientations, and frequencies. This method is applied on a number of visual and infrared images, every one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique set of texture `energy' features is typically associated with the segment containing the object(s) of interest.