Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot

One of the technologies in the industrial world that utilizes robots is the delivery of goods in warehouses, especially in the goods distribution process. This is very useful, especially in terms of resource efficiency and reducing human error. The existing system in this process usually uses the line follower concept on the robot's path with a camera sensor to determine the destination location. If the line and destination are not detected by the sensor or camera, the robot's navigation system will experience an error. it can happen if the sensor is dirty or the track is faded. The aim of this research is to develop a robot navigation system for efficient goods delivery in warehouses by integrating odometry and Dijkstra's algorithm for path planning. Holonomic robot is a robot that moves freely without changing direction to produce motion with high mobility. Dijkstra's algorithm is added to the holonomic robot to obtain the fastest trajectory. by calculating the distance of the node that has not been passed from the initial position, if in the calculation the algorithm finds a shorter distance it will be stored as a new route replacing the previously recorded route. the distance traversed by the djikstra algorithm is 780 mm while a distance of 1100 mm obtains the other routes. The time for using the Djikstra method is proven to be 5.3 seconds faster than the track without the Djikstra method with the same speed. Uneven track terrain can result in a shift in the robot's position so that it can affect the travel data. The conclusion is that odometry and Dijkstra's algorithm as a planning system and finding the shortest path are very efficient for warehouse robots to deliver goods than ordinary line followers without Dijkstra, both in terms of distance and travel time

A novel human-machine interface for guiding : the NeoASAS Smart Walker

In an aging society it is extremely important to develop devices, which can support and aid the elderly in their daily life. This demands tools that extend independent living and promote improved health. In this work it is proposed a new interface approach integrated into a walker. This interface is based on a joystick and it is intended to extract the user’s movement intentions. The interface is designed to be userfriendly, simple and intuitive, efficient and economic, meeting usability aspects and focused on a commercial implementation, but not being demanding at the user cognitive level. Preliminary sets of experiments were performed which showed the sensibility of the joystick to extract navigation commands from the user. These signals presented a higher frequency component that was attenuated by a Benedict-Bordner g-h filter. The presented methodology offers an effective cancelation of the undesired components from joystick data, allowing the system to extract in real-time voluntary user’s navigation commands. Based on this real-time identification of voluntary user’s commands, an approach to the control architecture of the robotic walker is being developed, in order to obtain stable and safe user assisted locomotion.(undefined

Active Training and Assistance Device for an Individually Adaptable Strength and Coordination Training

Das Altern der Weltbevölkerung, insbesondere in der westlichen Welt, stellt die Menschheit vor eine große Herausforderung. Zu erwarten sind erhebliche Auswirkungen auf den Gesundheitssektor, der im Hinblick auf eine steigende Anzahl von Menschen mit altersbedingtem körperlichem und kognitivem Abbau und dem damit erhöhten Bedürfnis einer individuellen Versorgung vor einer großen Aufgabe steht. Insbesondere im letzten Jahrhundert wurden viele wissenschaftliche Anstrengungen unternommen, um Ursache und Entwicklung altersbedingter Erkrankungen, ihr Voranschreiten und mögliche Behandlungen, zu verstehen. Die derzeitigen Modelle zeigen, dass der entscheidende Faktor für die Entwicklung solcher Krankheiten der Mangel an sensorischen und motorischen Einflüssen ist, diese wiederum sind das Ergebnis verringerter Mobilität und immer weniger neuer Erfahrungen. Eine Vielzahl von Studien zeigt, dass erhöhte körperliche Aktivität einen positiven Effekt auf den Allgemeinzustand von älteren Erwachsenen mit leichten kognitiven Beeinträchtigungen und den Menschen in deren unmittelbarer Umgebung hat. Diese Arbeit zielt darauf ab, älteren Menschen die Möglichkeit zu bieten, eigenständig und sicher ein individuelles körperliches Training zu absolvieren. In den letzten zwei Jahrzehnten hat die Forschung im Bereich der robotischen Bewegungsassistenten, auch Smarte Rollatoren genannt, den Fokus auf die sensorische und kognitive Unterstützung für ältere und eingeschränkte Personen gesetzt. Durch zahlreiche Bemühungen entstand eine Vielzahl von Ansätzen zur Mensch-Rollator-Interaktion, alle mit dem Ziel, Bewegung und Navigation innerhalb der Umgebung zu unterstützen. Aber trotz allem sind Trainingsmöglichkeiten zur motorischen Aktivierung mittels Smarter Rollatoren noch nicht erforscht. Im Gegensatz zu manchen Smarten Rollatoren, die den Fokus auf Rehabilitationsmöglichkeiten für eine bereits fortgeschrittene Krankheit setzen, zielt diese Arbeit darauf ab, kognitive Beeinträchtigungen in einem frühen Stadium soweit wie möglich zu verlangsamen, damit die körperliche und mentale Fitness des Nutzers so lang wie möglich aufrechterhalten bleibt. Um die Idee eines solchen Trainings zu überprüfen, wurde ein Prototyp-Gerät namens RoboTrainer-Prototyp entworfen, eine mobile Roboter-Plattform, die mit einem zusätzlichen Kraft-Momente-Sensor und einem Fahrradlenker als Eingabe-Schnittstelle ausgestattet wurde. Das Training beinhaltet vordefinierte Trainingspfade mit Markierungen am Boden, entlang derer der Nutzer das Gerät navigieren soll. Der Prototyp benutzt eine Admittanzgleichung, um seine Geschwindigkeit anhand der Eingabe des Nutzers zu berechnen. Desweiteren leitet das Gerät gezielte Regelungsaktionen bzw. Verhaltensänderungen des Roboters ein, um das Training herausfordernd zu gestalten. Die Pilotstudie, die mit zehn älteren Erwachsenen mit beginnender Demenz durchgeführt wurde, zeigte eine signifikante Steigerung ihrer Interaktionsfähigkeit mit diesem Gerät. Sie bewies ebenfalls den Nutzen von Regelungsaktionen, um die Komplexität des Trainings ständig neu anzupassen. Obwohl diese Studie die Durchführbarkeit des Trainings zeigte, waren Grundfläche und mechanische Stabilität des RoboTrainer-Prototyps suboptimal. Deswegen fokussiert sich der zweite Teil dieser Arbeit darauf, ein neues Gerät zu entwerfen, um die Nachteile des Prototyps zu beheben. Neben einer erhöhten mechanischen Stabilität, ermöglicht der RoboTrainer v2 eine Anpassung seiner Grundfläche. Dieses spezifische Merkmal der Smarten Rollatoren dient vor allem dazu, die Unterstützungsfläche für den Benutzer anzupassen. Das ermöglicht einerseits ein agiles Training mit gesunden Personen und andererseits Rehabilitations-Szenarien bei Menschen, die körperliche Unterstützung benötigen. Der Regelungsansatz für den RoboTrainer v2 erweitert den Admittanzregler des Prototypen durch drei adaptive Strategien. Die erste ist die Anpassung der Sensitivität an die Eingabe des Nutzers, abhängig von der Stabilität des Nutzer-Rollater-Systems, welche Schwankungen verhindert, die dann passieren können, wenn die Hände des Nutzers versteifen. Die zweite Anpassung beinhaltet eine neuartige nicht-lineare, geschwindigkeits-basierende Änderung der Admittanz-Parameter, um die Wendigkeit des Rollators zu erhöhen. Die dritte Anpassung erfolgt vor dem eigentlichen Training in einem Parametrierungsprozess, wo nutzereigene Interaktionskräfte gemessen werden, um individuelle Reglerkonstanten fein abzustimmen und zu berechnen. Die Regelungsaktionen sind Verhaltensänderungen des Gerätes, die als Bausteine für unterstützende und herausfordernde Trainingseinheiten mit dem RoboTrainer dienen. Sie nutzen das virtuelle Kraft-Feld-Konzept, um die Bewegung des Gerätes in der Trainingsumgebung zu beeinflussen. Die Bewegung des RoboTrainers wird in der Gesamtumgebung durch globale oder, in bestimmten Teilbereichen, durch räumliche Aktionen beeinflusst. Die Regelungsaktionen erhalten die Absicht des Nutzers aufrecht, in dem sie eine unabhängige Admittanzdynamik implementieren, um deren Einfluss auf die Geschwindigkeit des RoboTrainers zu berechnen. Dies ermöglicht die entscheidende Trennung von Reglerzuständen, um während des Trainings passive und sichere Interaktionen mit dem Gerät zu erreichen. Die oben genannten Beiträge wurden getrennt ausgewertet und in zwei Studien mit jeweils 22 bzw. 13 jungen, gesunden Erwachsenen untersucht. Diese Studien ermöglichen einen umfassenden Einblick in die Zusammenhänge zwischen unterschiedlichen Funktionalitäten und deren Einfluss auf die Nutzer. Sie bestätigen den gesamten Ansatz, sowie die gemachten Vermutungen im Hinblick auf die Gestaltung einzelner Teile dieser Arbeit. Die Einzelergebnisse dieser Arbeit resultieren in einem neuartigen Forschungsgerät für physische Mensch-Roboter-Interaktionen während des Trainings mit Erwachsenen. Zukünftige Forschungen mit dem RoboTrainer ebnen den Weg für Smarte Rollatoren als Hilfe für die Gesellschaft im Hinblick auf den bevorstehenden demographischen Wandel

Collaborative human-machine interfaces for mobile manipulators.

The use of mobile manipulators in service industries as both agents in physical Human Robot Interaction (pHRI) and for social interactions has been on the increase in recent times due to necessities like compensating for workforce shortages and enabling safer and more efficient operations amongst other reasons. Collaborative robots, or co-bots, are robots that are developed for use with human interaction through direct contact or close proximity in a shared space with the human users. The work presented in this dissertation focuses on the design, implementation and analysis of components for the next-generation collaborative human machine interfaces (CHMI) needed for mobile manipulator co-bots that can be used in various service industries. The particular components of these CHMI\u27s that are considered in this dissertation include: Robot Control: A Neuroadaptive Controller (NAC)-based admittance control strategy for pHRI applications with a co-bot. Robot state estimation: A novel methodology and placement strategy for using arrays of IMUs that can be embedded in robot skin for pose estimation in complex robot mechanisms. User perception of co-bot CHMI\u27s: Evaluation of human perceptions of usefulness and ease of use of a mobile manipulator co-bot in a nursing assistant application scenario. To facilitate advanced control for the Adaptive Robotic Nursing Assistant (ARNA) mobile manipulator co-bot that was designed and developed in our lab, we describe and evaluate an admittance control strategy that features a Neuroadaptive Controller (NAC). The NAC has been specifically formulated for pHRI applications such as patient walking. The controller continuously tunes weights of a neural network to cancel robot non-linearities, including drive train backlash, kinematic or dynamic coupling, variable patient pushing effort, or slope surfaces with unknown inclines. The advantage of our control strategy consists of Lyapunov stability guarantees during interaction, less need for parameter tuning and better performance across a variety of users and operating conditions. We conduct simulations and experiments with 10 users to confirm that the NAC outperforms a classic Proportional-Derivative (PD) joint controller in terms of resulting interaction jerk, user effort, and trajectory tracking error during patient walking. To tackle complex mechanisms of these next-gen robots wherein the use of encoder or other classic pose measuring device is not feasible, we present a study effects of design parameters on methods that use data from Inertial Measurement Units (IMU) in robot skins to provide robot state estimates. These parameters include number of sensors, their placement on the robot, as well as noise properties on the quality of robot pose estimation and its signal-to-noise Ratio (SNR). The results from that study facilitate the creation of robot skin, and in order to enable their use in complex robots, we propose a novel pose estimation method, the Generalized Common Mode Rejection (GCMR) algorithm, for estimation of joint angles in robot chains containing composite joints. The placement study and GCMR are demonstrated using both Gazebo simulation and experiments with a 3-DoF robotic arm containing 2 non-zero link lengths, 1 revolute joint and a 2-DoF composite joint. In addition to yielding insights on the predicted usage of co-bots, the design of control and sensing mechanisms in their CHMI benefits from evaluating the perception of the eventual users of these robots. With co-bots being only increasingly developed and used, there is a need for studies into these user perceptions using existing models that have been used in predicting usage of comparable technology. To this end, we use the Technology Acceptance Model (TAM) to evaluate the CHMI of the ARNA robot in a scenario via analysis of quantitative and questionnaire data collected during experiments with eventual uses. The results from the works conducted in this dissertation demonstrate insightful contributions to the realization of control and sensing systems that are part of CHMI\u27s for next generation co-bots

Instrumentation and validation of a robotic cane for transportation and fall prevention in patients with affected mobility

Dissertação de mestrado integrado em Engenharia Física, (especialização em Dispositivos, Microssistemas e Nanotecnologias)O ato de andar é conhecido por ser a forma primitiva de locomoção do ser humano, sendo que este traz muitos benefícios que motivam um estilo de vida saudável e ativo. No entanto, há condições de saúde que dificultam a realização da marcha, o que por consequência pode resultar num agravamento da saúde, e adicionalmente, levar a um maior risco de quedas. Nesse sentido, o desenvolvimento de um sistema de deteção e prevenção de quedas, integrado num dispositivo auxiliar de marcha, seria essencial para reduzir estes eventos de quedas e melhorar a qualidade de vida das pessoas. Para ultrapassar estas necessidades e limitações, esta dissertação tem como objetivo validar e instrumentar uma bengala robótica, denominada Anti-fall Robotic Cane (ARCane), concebida para incorporar um sistema de deteção de quedas e um mecanismo de atuação que possibilite a prevenção de quedas, ao mesmo tempo que assiste a marcha. Para esse fim, foi realizada uma revisão do estado da arte em bengalas robóticas para adquirir um conhecimento amplo e aprofundado dos componentes, mecanismos e estratégias utilizadas, bem como os protocolos experimentais, principais resultados, limitações e desafios em dispositivos existentes. Numa primeira fase, foi estipulado o objetivo de: (i) adaptar a missão do produto; (ii) estudar as necessidades do consumidor; e (iii) atualizar as especificações alvo da ARCane, continuação do trabalho de equipa, para obter um produto com design e engenharia compatível com o mercado. Foi depois estabelecida a arquitetura de hardware e discutidos os componentes a ser instrumentados na ARCane. Em seguida foram realizados testes de interoperabilidade a fim de validar o funcionamento singular e coletivo dos componentes. Relativamente ao controlo de movimento, foi desenvolvido um sistema inovador, de baixo custo e intuitivo, capaz de detetar a intenção do movimento e de reconhecer as fases da marcha do utilizador. Esta implementação foi validada com seis voluntários saudáveis que realizaram testes de marcha com a ARCane para testar sua operabilidade num ambiente de contexto real. Obteve-se uma precisão de 97% e de 90% em relação à deteção da intenção de movimento e ao reconhecimento da fase da marcha do utilizador. Por fim, foi projetado um método de deteção de quedas e mecanismo de prevenção de quedas para futura implementação na ARCane. Foi ainda proposta uma melhoria do método de deteção de quedas, de modo a superar as limitações associadas, bem como a proposta de dispositivos de deteção a serem implementados na ARCane para obter um sistema completo de deteção de quedas.The act of walking is known to be the primitive form of the human being, and it brings many benefits that motivate a healthy and active lifestyle. However, there are health conditions that make walking difficult, which, consequently, can result in worse health and, in addition, lead to a greater risk of falls. Thus, the development of a fall detection and prevention system integrated with a walking aid would be essential to reduce these fall events and improve people quality of life. To overcome these needs and limitations, this dissertation aims to validate and instrument a cane-type robot, called Anti-fall Robotic Cane (ARCane), designed to incorporate a fall detection system and an actuation mechanism that allow the prevention of falls, while assisting the gait. Therefore, a State-of-the-Art review concerning robotic canes was carried out to acquire a broad and in-depth knowledge of the used components, mechanisms and strategies, as well as the experimental protocols, main results, limitations and challenges on existing devices. On a first stage, it was set an objective to (i) enhance the product's mission statement; (ii) study the consumer needs; and (iii) update the target specifications of the ARCane, extending teamwork, to obtain a product with a market-compatible design and engineering that meets the needs and desires of the ARCane users. It was then established the hardware architecture of the ARCane and discussed the electronic components that will instrument the control, sensory, actuator and power units, being afterwards subjected to interoperability tests to validate the singular and collective functioning of cane components altogether. Regarding the motion control of robotic canes, an innovative, cost-effective and intuitive motion control system was developed, providing user movement intention recognition, and identification of the user's gait phases. This implementation was validated with six healthy volunteers who carried out gait trials with the ARCane, in order to test its operability in a real context environment. An accuracy of 97% was achieved for user motion intention recognition and 90% for user gait phase recognition, using the proposed motion control system. Finally, it was idealized a fall detection method and fall prevention mechanism for a future implementation in the ARCane, based on methods applied to robotic canes in the literature. It was also proposed an improvement of the fall detection method in order to overcome its associated limitations, as well as detection devices to be implemented into the ARCane to achieve a complete fall detection system

Surface Electromyography and Artificial Intelligence for Human Activity Recognition - A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.publishedVersio

Bipedial Locomotion Up Sandy Slopes: Systematic Experiments Using Zero Moment Point Methods

Bipedal robotic locomotion in granular media presents a unique set of challenges at the intersection of granular physics and robotic locomotion. In this paper, we perform a systematic experimental study in which biped robotic gaits for traversing a sandy slope are empirically designed using Zero Moment Point (ZMP) methods. We are able to implement gaits that allow our 7 degree-of-freedom planar walking robot to ascend slopes with inclines up to 10°. Firstly, we identify a given set of kinematic parameters that meet the ZMP stability criterion for uphill walking at a given angle. We then find that further relating the step lengths and center of mass heights to specific slope angles through an interpolated fit allows for significantly improved success rates when ascending a sandy slope. Our results provide increased insight into the design, sensitivity and robustness of gaits on granular material, and the kinematic changes necessary for stable locomotion on complex media

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC