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This thesis mainly consists of five independent papers concerning the reactive control design of autonomous mobile robots in the context of target tracking and cooperative formation keeping with obstacle avoidance in the static/dynamic environment. Technical contents of this thesis are divided into three parts. The first part consists of the first two papers, which consider the target-tracking and obstacle avoidance in the static environment. Especially, in the static environment, a fundamental issue of reactive control design is the local minima problem(LMP) inherent in the potential field methods(PFMs). Through introducing a state-dependent planned goal, the first paper proposes a switching control strategy to tackle this problem. The control law for the planned goal is presented. When trapped into local minima, the robot can escape from local minima by following the planned goal. The proposed control law also takes into account the presence of possible saturation constraints. In addition, a time-varying continuous control law is proposed in the second paper to tackle this problem. Challenges of finding continuous control solutions of LMP are discussed and explicit design strategies are then proposed. The second part of this thesis deals with target-tracking and obstacle avoidance in the dynamic environment. In the third paper, a reactive control design is presented for omnidirectional mobile robots with limited sensor range to track targets while avoiding static and moving obstacles in a dynamically evolving environment. Towards this end, a multiiii objective control problem is formulated and control is synthesized by generating a potential field force for each objective and combining them through analysis and design. Different from standard potential field methods, the composite potential field described in this paper is time-varying and planned to account for moving obstacles and vehicle motion. In order to accommodate a larger class of mobile robots, the fourth paper proposes a reactive control design for unicycle-type mobile robots. With the relative motion among the mobile robot, targets, and obstacles being formulated in polar coordinates, kinematic control laws achieving target-tracking and obstacle avoidance are synthesized using Lyapunov based technique, and more importantly, the proposed control laws also take into account possible kinematic control saturation constraints. The third part of this thesis investigates the cooperative formation control with collision avoidance. In the fifth paper, firstly, the target tracking and collision avoidance problem for a single agent is studied. Instead of directly extending the single agent controls to the multiagents case, the single agent controls are incorporated with the cooperative control design presented in [1]. The proposed decentralized control is reactive, considers the formation feedback and changes in the communication networks. The proposed control is based on a potential field method, its inherent oscillation problem is also studied to improve group transient performance.

[pt] Embora os sistemas multimídia tradicionais se concentrem na codificação e no armazenamento eficientes de tipos de mídia e suas relações temporais, a demanda atual por experiências mais ricas e personalizadas exige uma compreensão mais profunda do conteúdo semântico dessas mídias. Neste estudo, propomos a integração do processamento de nível semântico aos sistemas multimídia, enriquecendo o conteúdo com informações sobre entidades do mundo real, como objetos, ações, agentes e interpretação de linguagem. A principal contribuição desta dissertação é a apresentação de uma arquitetura para enriquecimento de dados multimídia em tempo real que usa técnicas de aprendizado de máquina para extrair representações semânticas incorporando as ao fluxos de dados multimídia como um serviço nativo e básico. Para demonstrar concretamente a proposta, implementamos dois casos de uso que servem como provas de conceito, mostrando a viabilidade da arquitetura e sua eficácia em cenários práticos. / [en] While traditional multimedia systems focused on efficient coding and storage of media types and their temporal relationships, the current demand for rich and customized experiences calls for a deeper understanding of semantic content. In this study, we propose the integration of semantic-level processing into multimedia systems, enriching content with information about real-world entities, such as objects, actions, agents, and language interpretation. The main contribution of this dissertation is the proposal of an architecture for real-time multimedia data enhancement that is able to use machine learning techniques to extract semantic representations and incorporating it into multimedia data streams as a native and basic service. To provide a concrete demonstration of the proposal, we implement two use cases that serve as proofs-of-concept, showing the feasibility of the architecture and showcasing its effectiveness in practical scenarios.

Industry 4.0 is a new phase of industrial growth that has been ushered in by the quick development of digital technologies like the Internet of Things (IoT), artificial intelligence (AI), and robots. Collaborative robotic products have appeared in this changing environment, enabling robots to collaborate with people in open workspaces. The paradigm changes away from autonomous robotics and toward collaborative human-robot interaction (HRI) has made it necessary to look at novel ways to improve output, effectiveness, and security. Many benefits, including more autonomy and flexibility, have been made possible by the introduction of Autonomous Mobile Robots (AMRs) and later Automated Guided Vehicles (AGVs) for material handling. However, this incorporation of robots into communal workspaces also brings up safety issues that must be taken into account. This thesis aims to address potential threats arising from the increasing automation in shopfloors and shared workplaces between AMRs and human operators by exploring the capabilities of Mixed Reality (MR) technologies. By harnessing MR's capabilities, the aim is to mitigate safety concerns and optimize the effectiveness of collaborative environments. To achieve this the research is structured around the following sub-objectives: the development of a communication network enabling interaction among all devices in the shared workspace and the creation of a MR user interface promoting accessibility for human operators. A comprehensive literature review was conducted to analyse existing proposals aimed at improving HRI through various techniques and approaches. The objective was to leverage MR technologies to enhance collaboration and address safety concerns, thereby ensuring the smooth integration of AMRs into shared workspaces. While the literature review revealed limited research utilizing MR for data visualization in this specific domain, the goal of this thesis was to go beyond existing solutions by developing a comprehensive approach that prioritizes safety and facilitates operator adaptation. The research findings highlight the superiority of MR in displaying critical information regarding robot intentions and identifying safe zones with reduced AMR activity. The utilization of HoloLens 2 devices, known for their ergonomic design, ensures operator comfort during extended use while enhancing the accuracy of tracking positions and intentions in highly automated environments. The presented information is designed to be concise, customizable, and easily comprehensible, preventing information overload for operators.  The implementation of MR technologies within shared workspaces necessitates ethical considerations, including transparent data collection and user consent. Building trust is essential to establish MR as a reliable tool that enhances operator working conditions and safety. Importantly, the integration of MR technologies does not pose a threat to job displacement but rather facilitates the smooth adaptation of new operators to collaborative environments. The implemented features augment existing safety protocols without compromising efficacy, resulting in an overall improvement in safety within the collaborative workspace. In conclusion, this research showcases the effectiveness of MR technologies in bolstering HRI, addressing safety concerns, and enhancing operator working conditions within collaborative shopfloor environments. Despite encountering limitations in terms of time, complexity, and available information, the developed solution showcases the potential for further improvements. The chosen methodology and philosophical paradigm have successfully attained the research objectives, and crucial ethical considerations have been addressed. Ultimately, this thesis proposes and provides a comprehensive explanation for potential future implementations, aiming to expand the actual capabilities of the solution.

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Autonomous vehicles are the subject of intense research because they are a safe and convenient alternative to present-day vehicles. Human drivers base their navigational decisions primarily on visual information and researchers have been attempting to use computers to do the same. The current challenge in using computer vision lies not in the collection or transmission of visual data, but in the perception of visual data to extract from it useful information. The focus of this thesis is on the use of computer vision to navigate an autonomous vehicle that will participate in the Intelligent Ground Vehicle Competition (IGVC.) This document starts with a description of the IGVC and the software design of an autonomous vehicle. This thesis then focuses on the weakest link in the system - the computer vision module. Vehicles at the IGVC are expected to autonomously navigate an obstacle course. Competing vehicles need to recognize and stay between lines painted on grass or pavement. The research presented in this document describes two methods used for boundary line extraction: color-based object extraction, and shape analysis for line recognition. This is the first time a combination of these methods is being applied to the problem of line recognition in the context of the IGVC. The most significant contribution of this work is a method for extracting lines in a binary image even when the line is attached to a shape that is not a line. Novel methods have been used to simplify camera calibration, and for perspective correction of the image. The results give promise of vastly improved autonomous vehicle performance. / Master of Science

Active docking in modular robotic systems has received a lot of interest recently as it allows small versatile robotic systems to coalesce and achieve the structural benefits of larger robotic systems. This feature enables reconfigurable modular robotic systems to bridge the gap between small agile systems and larger robotic systems. The proposed self-reconfigurable mobile robot design exhibits dual mobility using a tracked drive for longitudinal locomotion and wheeled drive for lateral locomotion. The two degrees of freedom (DOF) docking interface referred to as GHEFT (Genderless, High strength, Efficient, Fail-Safe, high misalignment Tolerant) allows for an efficient docking while tolerating misalignments in 6-DOF. In addition, motion along the vertical axis is also achieved via an additional translational DOF, allowing for toggling between tracked and wheeled locomotion modes by lowering and raising the wheeled assembly. This thesis also presents a visual-based onboard Hybrid Target Tracking algorithm to detect and follow a target robot leading to autonomous docking between the modules. As a result of this proposed approach, the tracked features are then used to bring the robots in sufficient proximity for the docking procedure using Image Based Visual Servoing (IBVS) control. Experimental results to validate the robustness of the proposed tracking method, as well as the reliability of the autonomous docking procedure, are also presented in this thesis. / Master of Science / Active docking in modular robotic systems has received a lot of interest recently as it allows small versatile robotic systems to coalesce and achieve the structural benefits of larger robotic systems. This feature enables reconfigurable modular robotic systems to bridge the gap between small agile systems and larger robotic systems. Such robots can prove useful in environments that are either too dangerous or inaccessible to humans. Therefore, in this research, several specific hardware and software development aspects related to self-reconfigurable mobile robots are proposed. In terms of hardware development, a robotic module was designed that is symmetrically invertible and exhibits dual mobility using a tracked drive for longitudinal locomotion and wheeled drive for lateral locomotion. Such interchangeable mobility is important when the robot operates in a constrained workspace. The mobile robot also has integrated two degrees of freedom (DOF) docking mechanisms referred to as GHEFT (Genderless, High strength, Efficient, Fail-Safe, high misalignment Tolerant). The docking interface allows for an efficient docking while tolerating misalignments in 6-DOF. In addition, motion along the vertical axis is also performed via an additional translational DOF, allowing for lowering and raising the wheeled assembly. The robot is equipped with sensors to provide positional feedback of the joints relative to the target robot. In terms of software development, a visual-based onboard Hybrid Target Tracking algorithm for high-speed consistent tracking iv of colored targets is also presented in this work. The proposed technique is used to detect and follow a colored target attached to the target robot leading to autonomous docking between the modules using Image Based Visual Servoing (IBVS). Experimental results to validate the robustness of the proposed tracking approach, as well as the reliability of the autonomous docking procedure, are also presented in the thesis. The thesis is concluded with discussions about future research in both structured and unstructured terrains.

Control systems for robotic manipulators have been investigated for several years. The difficulty in designing a controller for a robotic manipulator is due to the highly nonlinear, time-varying dynamics. Closed-loop constant gain controllers are effective when the robot is expected to perform a limited range of operations. In the case of a mobile robot, the commanded tasks are not likely to be repetitive. Thus, another method of control is desired to overcome the effects of the nonlinear time-varying dynamics. Several adaptive control methods have been applied to robotic manipulators. The adaptive controllers are successful at trajectory tracking in the presence of the nonlinear time-varying dynamics. Some of these methods are computationally demanding, therefore, most of the current research focuses on efficient adaptive control methods. In particular, the area of decentralized adaptive control is gaining popularity. This method involves reducing a dynamic system into subsystems, each with an individual controller. This method is more efficient since the controllers can operate simultaneously. In this study, a decentralized model reference adaptive controller (MRAC) was designed for a four-degree-of-freedom mobile robot. The performance of the decentralized MRAC controller was compared to that of a constant gain state feedback controller. The decentralized MRAC control strategy proved to be an efficient method of control for a mobile robot that is superior to state feedback control when the robot is performing highly nonlinear time-varying tasks. Also, the computational load for each subsystem of the decentralized controller was less than the computational load of the state feedback controller. / Master of Science

An unsupervised fuzzy logic navigation algorithm is designed and implemented in simulation for the Subsurface Explorer planetary exploration robot. The robot is intended for the subterranean exploration of Mars, and will be equipped with acoustic sensing for detecting obstacles. Measurements of obstacle distance and direction are anticipated to be imprecise however, since the performance of acoustic sensors is degraded in underground environments. Fuzzy logic is a satisfactory means of addressing imprecision in plant characteristics, and has been implemented in a variety of autonomous vehicle navigation applications. However, most fuzzy logic algorithms that perform well in unknown environments have large rule-bases or use complex methods for tuning fuzzy membership functions and rules. These qualities make them too computationally intensive to be used for planetary exploration robots like the SSX. In this thesis, we introduce an unsupervised fuzzy logic algorithm that can determine a trajectory for the SSX through unknown environments. This algorithm uses a combination of simple fusion of robot behaviors and self-tuning membership functions to determine robot navigation without resorting to the degree of complexity of previous fuzzy logic algorithms. Finally, we present some simulation results that demonstrate the practicality of our algorithm in navigating in different environments. The simulations justify the use of our fuzzy logic technique, and suggest future areas of research for fuzzy logic navigation algorithms. / Master of Science