Autonomous mobility for an electronic wheelchair

Despite the rapid development of medical technologies the health sector does not yet offer any universal remedy for people suffering from permanent impairment of motor functions. Individuals depending on the range of disability require rehabilitation and help to perform the ALDs (activities of daily living). To aid people affected by the impairment and relieve from some duties the ones responsible for helping them the electronic wheelchair was developed. One of the functions of the electronic wheelchair is supposed to be autonomous navigation with speech recognition. The main objective of this project was to extend the existing electronic wheelchair solution with all necessary equipment and software necessary to make the autonomous navigation possible. As a result, a versatile system was created capable of mapping the working space and navigating in both known and unknown dynamic environments. The system allows dynamic obstacle detection and avoidance, basic recovery behaviors and accepts navigation goals provided by speech recognition.A pesar del rápido desarrollo de las tecnologías médicas el sector de la salud todavía no ofrece ningún remedio universal para las personas sufriendo de falta de control motor. Dependiente del rango de discapacidad las personas requieren rehabilitación y ayuda para realizar AC (actividades cotidianas). Para ayudar a las personas afectadas por discapacidad y relevar de algunos deberes la gente que los soporta se desarrolló la silla de ruedas electrónica. Una de las funciones de ya mencionada silla de ruedas debería ser la navegación autónoma con reconocimiento de voz. Entonces el objetivo principal de este proyecto fue extender la solución existente con todo el hardware y software necesarios para que la navegación autónoma sea posible. El proyecto resultado en creación de un sistema versátil capaz de mapear el espacio de trabajo y navegar en entornos también conocidos y desconocidos. El sistema permite detección y evitación dinámica de obstáculos, soporta comportamientos básicos de recuperación y acepta objetivos de navegación proporcionados por el software de reconocimiento de voz

Design and Development of an FPGA-based Hardware Accelerator for Corner Feature Extraction and Genetic Algorithm-based SLAM System

Simultaneous Localization and Mapping (SLAM) systems are crucial parts of mobile robots. These systems require a large number of computing units, have significant real-time requirements and are also a vital factor which can determine the stability, operability and power consumption of robots. This thesis aims to improve the calculation speed of a lidar-based SLAM system in domestic scenes, reduce the power consumption of the SLAM algorithm, and reduce the overall cost of the whole platform. Lightweight, low-power and parallel optimization of SLAM algorithms are researched. In the thesis, two SLAM systems are designed and developed with a focus on energy-efficient and fast hardware-level design: a geometric method based on corner extraction and a genetic algorithm-based approach. Finally, an FPGA-based hardware accelerated SLAM is implemented and realized, and compared to a software-based system. As for the front-end SLAM system, a method of using a Corner Feature Extraction (CFE) algorithm on FPGA platforms is first proposed to improve the speed of the feature extraction. Considering building a back-end SLAM system with low power consumption, a SLAM system based on genetic algorithm combined with algorithms such as Extended Kalman Filter (EKF) and FastSLAM to reduce the amount of calculation in the SLAM system is also proposed. Finally, the thesis also proposes and implements an adaptive feature map which can replace a grid point map to reduce the amount of calculation and utilization of hardware resources. In this thesis, the lidar SLAM system with front-end and back-end parts mentioned above is implemented on the Xilinx PYNQ Z2 Platform. The implementation is operated on a mobile robot prototype and evaluated in real scenes. Compared with the implementation on the Raspberry Pi 3B+, the implementation in this thesis can save 86.25% of power consumption. The lidar SLAM system only takes 20 ms for location calculation in each scan which is 5.31 times faster compared with the software implementation with EKF

SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications

Modeling and Control for Vision Based Rear Wheel Drive Robot and Solving Indoor SLAM Problem Using LIDAR

abstract: To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design, control objectives for rear-wheel drive ground vehicles. Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform that can be used for conducting FAME research. A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities. The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability of commercially available vehicles costing over$2000. All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following summarizes the key hardware demonstrations presented and analyzed: (1)Cruise (v, ) control along a line, (2) Cruise (v, ) control along a curve, (3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle, (4) Finish the track with camera pan tilt structure in minimum time, (5) Finish the track without camera pan tilt structure in minimum time, (6) Vision based tracking performance with different cruise speed vx, (7) Vision based tracking performance with different camera fixed look-ahead distance L, (8) Vision based tracking performance with different delay Td from vision subsystem, (9) Manually remote controlled robot to perform indoor SLAM, (10) Autonomously line guided robot to perform indoor SLAM. For most cases, hardware data is compared with, and corroborated by, model based simulation data. In short, the thesis uses low-cost self-designed rear-wheel drive robot to demonstrate many capabilities that are critical in order to reach the longer-term FAME goal.Dissertation/ThesisDefense PresentationMasters Thesis Electrical Engineering 201

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%