1,720,992 research outputs found
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Robocart Machine Vision Framework
No full textThis project documents a framework for an extensible, flexible machine vision software implementation for the Robocart project. It uses a distributed mobile computing framework in order to best leverage the scalability of machine vision. This process aims to improve upon current machine vision implementations in commercial autonomous vehicles, as well as provide a basis for further development of Robocart’s autonomous navigation systems. This framework is tested with the use case of road detection
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Introductory Microcontroller Programming
No full textThis text is a treatise on microcontroller programming. It introduces the major peripherals found on most microcontrollers, including the usage of them, focusing on the ATmega644p in the AVR family produced by Atmel. General information and background knowledge on several topics is also presented. These topics include information regarding the hardware of a microcontroller and assembly code as well as instructions regarding good program structure and coding practices. Examples with code and discussion are presented throughout. This is intended for hobbyists and students desiring knowledge on programming microcontrollers, and is written at a level that students entering the junior level core robotics classes would find useful
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Real time implementation of SURF algorithm on FPGA platform
No full textToo many traffic accidents are caused by drivers’ failure of noticing buildings, traffic sign and other objects. Video based scene or object detection which can easily enhance drivers’ judgment performance by automatically detecting scene and signs. Two of the recent popular video detection algorithms are Background Differentiation and Feature based object detection. The background Differentiation is an efficient and fast way of observing a moving object in a relatively stationary background, which makes it easy to be implemented on a mobile platform and performs a swift processing speed. The Feature based scene detection such like the Speeded Up Robust Feature (SURF), is an appropriate way of detecting specific scene with accuracy and rotation and illumination invariance. By comparison, SURF computational expense is much higher, which remains the algorithm limited in real time mobile platform. In this thesis, I present two real time tracking algorithms, Differentiation based and SURF based scene detection systems on FPGA platform. The proposed hardware designs are able to process video of 800*600 resolution at 60 frames per second, the video clock rate is 40 MHz
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Perception Framework for Activities of Daily Living Manipulation Tasks
No full textThere is an increasing concern in tackling the problems faced by the elderly community and physically in-locked people to lead an independent life experience problems with self- care. The need for developing service robots that can help people with mobility impairments is hence very essential. Developing a control framework for shared human-robot autonomy will allow locked-in individuals to perform the Activities of Daily Living (ADL) in a exible way. The relevant ADL scenarios were identi ed as handling objects, self-feeding, and opening doors for indoor nav- igation assistance. Multiple experiments were conducted, which demonstrates that the robot executes these daily living tasks reliably without requiring adjustment to the environment. The indoor manipulation tasks hold the challenge of dealing with a wide range of unknown objects. This thesis presents a framework developed for grasping without requiring a priori knowledge of the objects being manipulated. A successful manipulation task requires the combination of aspects such as envi- ronment modeling, object detection with pose estimation, grasp planning, motion planning followed by an e�cient grasp execution, which is validated by a 6+2 Degree of Freedom robotic manipulator
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Cyber Physical System for Continuous Evaluation of Fall Risks to Enable Aging-In-Place
No full textEvery year, one out of three adults over the age of 65 falls, and about 30% of the falls result in moderate to severe injuries. The high rate of fall-related hospitalizations and the fact that falls are a major source of morbidity and mortality in older adults have motivated extensive interdisciplinary clinical and engineering research with a focus on fall prevention. This research is aimed at developing a medical Cyber Physical System (CPS) composed of a human supervised mobile robot and ambient intelligence sensors to provide continuous evaluation of environmental risks in the home. As a preventive measure to avoid falls, we propose use of mobile robots to detect possible fall risks inside a house. As a step-up to that, we also define a control framework for intelligent, networked mobile robots to semi-autonomously perform assistive and preventive tasks. This framework is integrated in a smart home that provides monitoring and control capabilities of environmental conditions such as objects blocking pathways or uneven surfaces. The main outcome of this work is the realization of this system at Worcester Polytechnic Institute's (WPI) @Home testbed
Modelling and control of two cooperating underwater vehicles with on -board manipulators
No full textTwo cooperating Autonomous Underwater Vehicles (AUVs), each equipped with a robotic arm on-board, can be advantageously used to perform underwater transportation tasks and to manipulate underwater objects such as pipelines, cables and mines. In this research, the modelling and control problem is studied for two Underwater Vehicle Manipulator (UVM) mechanisms holding a rigid object. The rigidbody equations of motion for each UVM mechanism are derived in the world coordinate frame using the Lagrange\u27s formulation. Then, the hydrodynamic effects, such as the added mass, drag and buoyancy as well as the forces due to the motions of the surrounding fluid are incorporated into the resulting equations of motion. Aforementioned hydrodynamic terms cannot be neglected because of the relatively high density of the medium (sea water) in which the system operates. Moreover, the kinematic and dynamic coupling terms due to the load are studied and included in the dynamical model. A reduced-order model whose formulation is due to the kinematic constraints is proposed for the control law design. The dynamical model both in the full and the reduced-order space contains parameter uncertainties such as the added mass and drag coefficients. An adaptive controller with parameter estimation is formulated in the reduced-order space. The controller design is based on the Lyapunov theory and guarantees the asymptotic stability of the states both in the reduced and full-order space. The applicability of the mathematical model and the adaptive controller are tested by computer simulations
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Automating Fullerton Fitness Test Using a Home Robot
No full textFitness is important to achieve day to day tasks in our lives. It is significantly more important for the elderly as the functionality of the body declines. Fullerton Fitness Test (FFT) is a set of exercises to assess the fitness of the elderly. It was developed at Fullerton University by Dr. Roberta Rikli and Dr. Jessie Jones as a part of the LifeSpan Wellness Program. Under FFT, an individual is asked to go through a certain range of motions and based on these motions, a physician assigns a score to each exercise in FFT. The individual’s fitness is assessed based on these scores. At present, FFT is performed in the presence of a trained physician. The overall goal of the research presented in this thesis is to assess an individual’s fitness using a depth sensor mounted differential drive robot based on FFT without the help of a physician as a trained physician might not be always available and even if one is available, having one around every time is expensive. The robot autonomously navigates through the testing facility, tracks the user, assists the user performing FFT and saves the data in user file for further evaluation. The results received from the FFT is evaluated to measure the performance of the user. This data is also used for book keeping purposes and to track the progress of the user. This research is also concerned with integrating this setup with a smart home facility where all the data is stored in a central server
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An Intelligent Portable Aerial Surveillance System: Modeling and Image Stitching
No full textUnmanned Aerial Vehicles (UAVs) have been widely used in modern warfare for surveillance, reconnaissance and even attack missions. They can provide valuable battlefield information and accomplish dangerous tasks with minimal risk of loss of lives and personal injuries. However, existing UAV systems are far from perfect to meet all possible situations. One of the most notable situations is the support for individual troops. Besides the incapability to always provide images in desired resolution, currently available systems are either too expensive for large-scale deployment or too heavy and complex for a single solder. Intelligent Portable Aerial Surveillance System (IPASS), sponsored by the Air Force Research Laboratory (AFRL), is aimed at developing a low-cost, light-weight unmanned aerial vehicle that can provide sufficient battlefield intelligence for individual troops. The main contributions of this thesis are two-fold (1) the development and verification of a model-based flight simulation for the aircraft, (2) comparison of image stitching techniques to provide a comprehensive aerial surveillance information from multiple vision. To assist with the design and control of the aircraft, dynamical models are established at different complexity levels. Simulations with these models are implemented in Matlab to study the dynamical characteristics of the aircraft. Aerial images acquired from the three onboard cameras are processed after getting the flying platform built. How a particular image is formed from a camera and the general pipeline of the feature-based image stitching method are first introduced in the thesis. To better satisfy the needs of this application, a homography-based stitching method is studied. This method can greatly reduce computation time with very little compromise in the quality of the panorama, which makes real-time video display of the surroundings on the ground station possible. By implementing both of the methods for image stitching using OpenCV, a quantitative comparison in the performance is accomplished
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An Embedded Seizure Onset Detection System
No full textA combined hardware and software platform for ambulatory seizure onset detection is presented. The hardware is developed around commercial off-the-shelf components, featuring ADS1299 analog front ends for electroencephalography from Texas Instruments and a Broadcom ARM11 microcontroller for algorithm execution. The onset detection algorithm is a patient-specific support vector machine algorithm. It outperforms a state-of-the-art detector on a reference data set, with 100% sensitivity, 3.4 second average onset detection latency, and on average 1 false positive per 24 hours. The more comprehensive European Epilepsy Database is then evaluated, which highlights several real-world challenges for seizure onset detection, resulting in reduced average sensitivity of 93.5%, 5 second average onset detection latency, and 85.5% specificity. Algorithm enhancements to improve this reduced performance are proposed
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Extended and Unscented Kalman Smoothing for Re-linearization of Nonlinear Problems with Applications
No full textThe Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF) are commonly implemented practical solutions for solving nonlinear state space estimation problems; all based on the linear state space estimator, the Kalman Filter. Often, the UKF and EnKF are cited as a superior methods to the EKF with respect to error-based performance criteria. The UKF in turn has the advantage over the EnKF of smaller computational complexity.
In practice however the UKF often fails to live up to this expectation, with performance which does not surpass the EKF and estimates which are not as robust as the EnKF. This work explores the geometry of alternative sigma point sets, which form the basis of the UKF, contributing several new sets along with novel methods used to generate them. In particular, completely novel systems of sigma points that preserve higher order statistical moments are found and evaluated. Additionally a new method for scaling and problem specific tuning of sigma point sets is introduced as well as a discussion of why this is necessary, and a new way of thinking about UKF systems in relation to the other two Kalman Filter methods. An Iterated UKF method is also introduced, similar to the smoothing iterates developed previously for the EKF. The performance of all of these methods is demonstrated using problem exemplars with the improvement of the contributed methods highlighted
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