A Real-Time Path Planner for a Smart Wheelchair Using Harmonic Potentials and a Rubber Band Model

We present an efficient path planner for smart wheelchairs based on harmonic potential fields. While the use of harmonic fields can always guarantee finding an existing path, they are extremely computational intensive and a sufficiently detailed map of the environment may lead to an unfeasible solution for the path. Also, since our target application is for the navigation of a smart wheelchair, for people with severe disabilities, the path provided by the harmonic field is frequently too sharp and needs to be smoothened. In order to address the first problem, we propose a parallel algorithm implemented using Graphics Processor Units (GPUs) on the Compute Unified Device Architecture (CUDA) platform. And for the second problem, we developed a rubber band model that provides extra forces to be added to the attracting forces of the harmonic fields. This model assumes that the path is an elastic line, a rubber band, connecting the source and destination points. This rubber band simulates the internal tension forces trying to tighten the line. As the result section demonstrates, both the original path from the harmonic field alone and the path smoothened by the rubber band model have approximate the same length, but the first path contains many bumps, sharp angles, and zig-zags, while the second one provides a much more comfortable ride for the passenger of the wheelchair. Either one is executed in real-time, allowing the proposed method to be used for real navigation of smart wheelchairs

Development of a pneumatic three finger gripper that will incorperate a force feedback control system [abstract]

Abstract only availableIn this project I was given the task of designing a three finger gripper. This gripper was to be mounted on one of the Puma 200 robotic arms that is currently in use in the Electrical and Computer Engineering robotics laboratory. The gripper was designed to have three fingers to increase its ability to handle irregularly shaped objects while keeping the design complexity to a minimum. A pneumatic actuator was chosen to drive this device in order to incorporate force feedback into the control of the gripper. Force feedback is desirable because it allows the gripper to apply minimal force to an object in order to pick it up without damaging it. With all of the details of the gripper designed, the next step will be to create the force feedback control system for the gripper so that it will be able to function in the classroom.College of Engineering Undergraduate Research Optio

GPU-Based Simulation of Cellular Neural Networks for Image Processing

The inherent massive parallelism of cellular neural networks makes them an ideal computational platform for kernelbased algorithms and image processing. General-purpose GPUs provide similar massive parallelism, but it can be difficult to design algorithms to make optimal use of the hardware. The presented research includes a GPU abstraction based on cellular neural networks. The abstraction offers a simplified view of massively parallel computation which remains reasonably efficient. An image processing library with visualization software has been developed to showcase the flexibility and power of cellular computation on GPUs. Benchmarks of the library indicate that commodity GPUs can be used to significantly accelerate CNN research and offer a viable alternative to CPU-based image processing algorithms

Analysis of Clipping Effect in Color Images Captured by CCD Cameras

Camera sensors (CCD) have a limited dynamic range that constrains the brightness of the incident light that can be quantified. In other words, if the ray of incident light is too intense, the sensor saturates and the value quantified is inadequately represented. This color clipping effect is a common problem in computer vision and it can become specially difficult when dealing with specular objects against a low-intensity background. In this paper, we present a method for analyzing such clipping effects of CCD cameras appearing in color images. Using an averaging technique to estimate the color of the illuminant, we define two types of axes in the RGB color cube: the Illumination Axis and the Clipping Axis. Our study concludes the followings: 1) the clipped pixels from a dielectric object form one or two lines, depending on the number of color channels on which the clipping effect takes place; and 2) these lines are parallel to the Clipping Axes. These two properties allow for a framework for a color-based segmentation that works even in the presence of saturated (specular) regions in the image. Moreover, the captured images can now be obtained under a wider variation of illumination conditions

A New 3D Representation and Compression Algorithm for Non-Rigid Moving Objects using Affine-Octree

This paper presents a new 3D representation for non-rigid objects using motion vectors between two consecutive frames. Our method relies on an Octree to recursively partition the object into smaller parts for which a small number of motion parameters can accurately represent that portion of the object. The partitioning continues as long as the respective motion parameters are insufficiently accurate to describe the object. Unlike other Octree methods, our method employs an affine transformation for the motion description part, which greatly reduces the storage. Finally, an adaptive thresholding, a singular value decomposition for dealing with singularities, and a quantization and arithmetic coding further enhance our proposed method by increasing the compression while maintaining very good signal-noise ratio. Compared with other methods like trilinear interpolation or Principle Component Analysis (PCA) based algorithm, the Affine-Octree method is easy to compute and highly compact. As the results demonstrate, our method has a better performance in terms of compression ratio and PSNR, while it remains simple

Homography-Based Ground Plane Detection for Mobile Robot Navigation Using a Modified EM Algorithm

In this paper, a homography-based approach for determining the ground plane using image pairs is presented. Our approach is unique in that it uses a Modified Expectation Maximization algorithm to cluster pixels on images as belonging to one of two possible classes: ground and non-ground pixels. This classification is very useful in mobile robot navigation because, by segmenting out the ground plane, we are left with all possible objects in the scene, which can then be used to implement many mobile robot navigation algorithms such as obstacle avoidance, path planning, target following, landmark detection, etc. Specifically, we demonstrate the usefulness and robustness of our approach by applying it to a target following algorithm. As the results section shows, the proposed algorithm for ground plane detection achieves an almost perfect detection rate (over 99%) despite the relatively higher number of errors in pixel correspondence from the feature matching algorithm used: SIFT

3D Human Modeling using Virtual Multi-View Stereopsis and Object-Camera Motion Estimation

This paper presents a method for multi-view 3D modeling of human bodies using virtual stereopsis. The algorithm expands and improves the method used in [5], but unlike that method, our approach does not require multiple calibrated cameras and/or carefully-positioned turn tables. Instead, an algorithm using SIFT feature extraction is employed and an accurate motion estimation is performed to calculate the position of virtual cameras around the object. That is, by employing a single pair of cameras mounted on a same tripod, our algorithm computes the relative pose between camera and object and creates virtual cameras from the consecutive images in the video sequence. Besides not requiring any special setup, another advantage of our method is in the simplicity to obtain denser models if necessary: by only increasing the number of sampled images during the object-camera motion. As the quantitative results presented here demonstrate, our method compares to the PMVS method, while it makes it much simpler and cost-effective to implement

Multiple Targets Geolocation Using SIFT and Stereo Vision on Airborne Video Sequences

We propose a robust and accurate method for multi-target geo-localization from airborne video. The difference between our approach and other approaches in the literature is fourfold: 1) it does not require gimbal control of the camera or any particular path planning control for the UAV; 2) it can instantaneously geolocate multiple targets even if they were not previously observed by the camera; 3) it does not require a georeferenced terrain database nor an altimeter for estimating the UAV's and the target's altitudes; and 4) it requires only one camera, but it employs a multi-stereo technique using the image sequence for increased accuracy in target geo-location. The only requirements for our approach are: that the intrinsic parameters of the camera be known; that the on board camera be equipped with global positioning system (GPS) and inertial measurement unit (IMU); and that enough feature points can be extracted from the surroundings of the target. Since the first two constraints are easily satisfied, the only real requirement is regarding the feature points. However, as we explain later, this last constraint can also be alleviated if the ground is approximately planar. The result is a method that can reach a few meters of accuracy for an UAV flying at a few hundred meters above the ground. Such performance is demonstrated by computer simulation, in-scale data using a model city, and real airborne video with ground truth

The Swarm Computer, an Analog Cellular-Swarm Hybrid Architecture

The “killer apps” of cellular and swarm computing are image processing and optimization, respectively; however, applying these platforms to general-purpose computing remains impractical. Designing systems within the restrictive framework of cellular automata is extremely difficult, though often very efficient and scalable. On the other hand, swarm networks are very powerful but difficult to implement in hardware. Here we introduce a hybrid model, the Swarm Computer, which is both practical to program and efficient to implement. Applications in astrophysics and image processing are considered