Image-guided Landmark-based Localization and Mapping with LiDAR

Mobile robots must be able to determine their position to operate effectively in diverse environments. The presented work proposes a system that integrates LiDAR and camera sensors and utilizes the YOLO object detection model to identify objects in the robot's surroundings. The system, developed in ROS, groups detected objects into triangles, utilizing them as landmarks to determine the robot's position. A triangulation algorithm is employed to obtain the robot's position, which generates a set of nonlinear equations that are solved using the Levenberg-Marquardt algorithm. The presented work comprehensively discusses the proposed system's study, design, and implementation. The investigation begins with an overview of current SLAM techniques. Next, the system design considers the requirements for localization and mapping tasks and an analysis comparing the proposed approach to the contemporary SLAM methods. Finally, we evaluate the system's effectiveness and accuracy through experimentation in the Gazebo simulation environment, which allows for controlling various disturbances that a real scenario can introduce

Heuristic localization and mapping for active sensing with humanoid robot NAO

The purpose of this thesis is to utilize vision system for autonomous navigation. The platform which has been used was an NAO humanoid robot. More specifically, NAO cameras and its makers have been used to solve the two most fundamental problems of autonomous mobile robots which are localization and mapping the environment. NAO markers have been printed and positioned on virtual walls to construct an experimental environment to investigate proposed localization and mapping methods. In algorithm side, basically NAO uses two known markers to localize itself and averages over all location predicted using each pair of known markers. At the same time NAO calculates the location of any unknown markers and add it to the Map. Moreover, A simple go-to-goal path planning algorithm has been implemented to provide a continuous localization and mapping for longer walks of NAO. The result of this work shows that NAO can navigate in an experimental environment using only its marker and camera and reach a predefined target location successfully. Also, It has been shown that NAO can locate itself with acceptable accuracy and make a feature-based map of markers at each location. This thesis provides a starting point for experimenting with different algorithms in path planning as well as possibility to investigate active sensing methods. Furthermore, the possibility of combining other features with NAO marker can be investigated to provide even more accurate result

Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation

In this work, a comparative study between an Ultra Wide-Band (UWB) localization system and a Simultaneous Localization and Mapping (SLAM) algorithm is presented. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy in range measurements based on Time of Arrival (TOA) estimation. SLAM algorithms recursively estimates the map of an environment and the pose (position and orientation) of a mobile robot within that environment. The comparative study presented here involves the performance analysis of implementing in parallel an UWB localization based system and a SLAM algorithm on a mobile robot navigating within an environment. Real time results as well as error analysis are also shown in this work

Sistemas de suporte à condução autónoma adequados a plataforma robótica 4-wheel skid-steer: percepção, movimento e simulação

As competições de robótica móvel desempenham papel preponderante na difusão da ciência e da engenharia ao público em geral. E também um espaço dedicado ao ensaio e comparação de diferentes estratégias e abordagens aos diversos desafios da robótica móvel. Uma das vertentes que tem reunido maior interesse nos promotores deste género de iniciativas e entre o público em geral são as competições de condução autónoma. Tipicamente as Competi¸c˜oes de Condução Autónoma (CCA) tentam reproduzir um ambiente semelhante a uma estrutura rodoviária tradicional, no qual sistemas autónomos deverão dar resposta a um conjunto variado de desafios que vão desde a deteção da faixa de rodagem `a interação com distintos elementos que compõem uma estrutura rodoviária típica, do planeamento trajetórias à localização. O objectivo desta dissertação de mestrado visa documentar o processo de desenho e concepção de uma plataforma robótica móvel do tipo 4-wheel skid-steer para realização de tarefas de condução autónoma em ambiente estruturado numa pista que pretende replicar uma via de circulação automóvel dotada de sinalética básica e alguns obstáculos. Paralelamente, a dissertação pretende também fazer uma análise qualitativa entre o processo de simulação e a sua transposição para uma plataforma robótica física. inferir sobre a diferenças de performance e de comportamento.Mobile robotics competitions play an important role in the diffusion of science and engineering to the general public. It is also a space dedicated to test and compare different strategies and approaches to several challenges of mobile robotics. One of the aspects that has attracted more the interest of promoters for this kind of initiatives and general public is the autonomous driving competitions. Typically, Autonomous Driving Competitions (CCAs) attempt to replicate an environment similar to a traditional road structure, in which autonomous systems should respond to a wide variety of challenges ranging from lane detection to interaction with distinct elements that exist in a typical road structure, from planning trajectories to location. The aim of this master’s thesis is to document the process of designing and endow a 4-wheel skid-steer mobile robotic platform to carry out autonomous driving tasks in a structured environment on a track that intends to replicate a motorized roadway including signs and obstacles. In parallel, the dissertation also intends to make a qualitative analysis between the simulation process and the transposition of the developed algorithm to a physical robotic platform, analysing the differences in performance and behavior

ASCCbot: An Open Mobile Robot Platform

ASCCbot, an open mobile platform built in ASCC lab, is presented in this thesis. The hardware and software design of the ASCCbot makes it a robust, extendable and duplicable robot platform which is suitable for most mobile robotics research including navigation, mapping, localization, etc. ROS is adopted as the major software framework, which not only makes ASCCbot a open-source project, but also extends its network functions so that multi-robot network applications can be easily implemented based on multiple ASCCbots. Collaborative localization is designed to test the network features of the ASCCbot. A telepresence robot is built based on the ASCCbot. A Kinect-based human gesture recognition method is implemented for intuitive human-robot interaction on it. For the telepresence robot, a GUI is also created in which basic control commands, video streaming and 2D metric map rendering are presented. Last but not least, semantic mapping through human activity recognition is proposed as a novel approach to semantic mapping. For the human activity recognition part, a power-aware wireless motion sensor is designed and evaluated. The overall semantic mapping system is explained and tested in a mock apartment. The experiment results show that the activity recognition results are reliable, and the semantic map updating process is able to create an accurate semantic map which matches the real furniture layout. To sum up, the ASCCbot is a versatile mobile robot platform with basic functions as well as feature functions implemented. Complex high-level functions can be built upon the existing functions from the ASCCbot. With its duplicability, extendability and open-source feature, the ASCCbot will be very useful for mobile robotics research.School of Electrical & Computer Engineerin

ROBOTIC SOUND SOURCE LOCALIZATION AND TRACKING USING BIO-INSPIRED MINIATURE ACOUSTIC SENSORS

Sound source localization and tracking using auditory systems has been widely investigated for robotics applications due to their inherent advantages over other systems, such as vision based systems. Most existing robotic sound localization and tracking systems utilize conventional microphone arrays with different arrangements, which are inherently limited by a size constraint and are thus difficult to implement on miniature robots. To overcome the size constraint, sensors that mimic the mechanically coupled ear of fly Ormia have been previously developed. However, there has not been any attempt to study robotic sound source localization and tracking with these sensors. In this dissertation, robotic sound source localization and tracking using the miniature fly-ear-inspired sensors are studied for the first time. First, through investigation into the Cramer Rao lower bound (CRLB) and variance of the sound incident angle estimation, an enhanced understanding of the influence of the mechanical coupling on the performance of the fly-ear inspired sensor for sound localization is achieved. It is found that due to the mechanical coupling between the membranes, at its working frequency, the fly-ear inspired sensor can achieve an estimation of incident angle that is 100 time better than that of the conventional microphone pair with same signal-to-noise ratio in detection of the membrane deflection. Second, development of sound localization algorithms that can be used for robotic sound source localization and tracking using the fly-ear inspired sensors is carried out. Two methods are developed to estimate the sound incident angle based on the sensor output. One is based on model-free gradient descent method and the other is based on fuzzy logic. In the first approach, different localization schemes and different objective functions are investigated through numerical simulations, in which two-dimensional sound source localization is achieved without ambiguity. To address the slow convergence due to the iterative nature of the first approach, a novel fuzzy logic model of the fly-ear sensor is developed in the second approach for sound incident angle estimation. This model is studied in both simulations and experiments for localization of a stationary source and tracking a moving source in one dimension with a good performance. Third, nonlinear and quadratic-linear controllers are developed for control of the kinematics of a robot for sound source localization and tracking, which is implemented later in a mobile platform equipped with a microphone pair. Both homing onto a stationary source and tracking of a moving source with pre-defined paths are successfully demonstrated. Through this dissertation work, new knowledge on robotic sound source localization and tracking using fly-ear inspired sensors is created, which can serve as a basis for future study of sound source localization and tracking with miniature robots

Whole-body Manipulation using Reinforcement Learning

Recent advancements in artificial intelligence(AI) have revolutionized the field of robotics. One of the most intriguing use cases in this domain is whole-body manipulation. Whole-body manipulation combines the precision of robotic manipulators with the expanded reach of mobile platforms. This thesis explores the task of autonomous whole-body manipulation using reinforcement learning (RL). By leveraging RL’s ability to learn from experience and adapt to new scenarios, we aim to navigate and manipulate a robot jointly. First, we explore RL for navigation and manipulation separately. After developing a keen understanding of these tasks and training successful RL agents, we move towards joint navigation and manipulation. We conduct experiments using different training methods to combine these tasks under the paradigm of hierarchical RL (HRL) to achieve autonomous whole-body manipulation. The resulting RL agent is capable of successfully reaching a target location outside the operating range of the arm without collisions. In conclusion, we provide an example of the future potential of HRL for complex tasks within the domain of robotics