21 research outputs found

    Mobile robot vavigation using a vision based approach

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    PhD ThesisThis study addresses the issue of vision based mobile robot navigation in a partially cluttered indoor environment using a mapless navigation strategy. The work focuses on two key problems, namely vision based obstacle avoidance and vision based reactive navigation strategy. The estimation of optical flow plays a key role in vision based obstacle avoidance problems, however the current view is that this technique is too sensitive to noise and distortion under real conditions. Accordingly, practical applications in real time robotics remain scarce. This dissertation presents a novel methodology for vision based obstacle avoidance, using a hybrid architecture. This integrates an appearance-based obstacle detection method into an optical flow architecture based upon a behavioural control strategy that includes a new arbitration module. This enhances the overall performance of conventional optical flow based navigation systems, enabling a robot to successfully move around without experiencing collisions. Behaviour based approaches have become the dominant methodologies for designing control strategies for robot navigation. Two different behaviour based navigation architectures have been proposed for the second problem, using monocular vision as the primary sensor and equipped with a 2-D range finder. Both utilize an accelerated version of the Scale Invariant Feature Transform (SIFT) algorithm. The first architecture employs a qualitative-based control algorithm to steer the robot towards a goal whilst avoiding obstacles, whereas the second employs an intelligent control framework. This allows the components of soft computing to be integrated into the proposed SIFT-based navigation architecture, conserving the same set of behaviours and system structure of the previously defined architecture. The intelligent framework incorporates a novel distance estimation technique using the scale parameters obtained from the SIFT algorithm. The technique employs scale parameters and a corresponding zooming factor as inputs to train a neural network which results in the determination of physical distance. Furthermore a fuzzy controller is designed and integrated into this framework so as to estimate linear velocity, and a neural network based solution is adopted to estimate the steering direction of the robot. As a result, this intelligent iv approach allows the robot to successfully complete its task in a smooth and robust manner without experiencing collision. MS Robotics Studio software was used to simulate the systems, and a modified Pioneer 3-DX mobile robot was used for real-time implementation. Several realistic scenarios were developed and comprehensive experiments conducted to evaluate the performance of the proposed navigation systems. KEY WORDS: Mobile robot navigation using vision, Mapless navigation, Mobile robot architecture, Distance estimation, Vision for obstacle avoidance, Scale Invariant Feature Transforms, Intelligent framework

    Advanced Mobile Robotics: Volume 3

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    Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

    Robotic Caregivers -- Simulation and Capacitive Servoing for Physical Human-Robot Interaction

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    Physical human-robot interaction and robotic assistance presents an opportunity to benefit the lives of many people, including the millions of older adults and people with physical disabilities, who have difficulty performing activities of daily living (ADLs) on their own. Robotic caregiving for activities of daily living could increase the independence of people with disabilities, improve quality of life, and help address global societal issues, such as aging populations, high healthcare costs, and shortages of healthcare workers. Yet, robotic assistance presents several challenges, including risks associated with physical human-robot interaction, difficulty sensing the human body, and complexities of modeling deformable materials (e.g. clothes). We address these challenges through techniques that span the intersection of machine learning, physics simulation, sensing, and physical human-robot interaction. Haptic Perspective-taking: We first demonstrate that by enabling a robot to predict how its future actions will physically affect a person (haptic perspective-taking), robots can provide safer assistance, especially within the context of robot-assisted dressing and manipulating deformable clothes. We train a recurrent model consisting of both a temporal estimator and predictor that allows a robot to predict the forces a garment is applying onto a person using haptic measurements from the robot's end effector. By combining this predictor with model predictive control (MPC), we observe emergent behaviors that result in the robot navigating a garment up a person's entire arm. Capacitive Sensing for Tracking Human Pose: Towards the goal of robots performing robust and intelligent physical interactions with people, it is crucial that robots are able to accurately sense the human body, follow trajectories around the body, and track human motion. We have introduced a capacitive servoing control scheme that allows a robot to sense and navigate around human limbs during close physical interactions. Capacitive servoing leverages temporal measurements from a capacitive sensor mounted on a robot's end effector to estimate the relative pose of a nearby human limb. Capacitive servoing then uses these human pose estimates within a feedback control loop in order to maneuver the robot's end effector around the surface of a human limb. Through studies with human participants, we have demonstrated that these sensors can enable a robot to track human motion in real time while providing assistance with dressing and bathing. We have also shown how these sensors can benefit a robot providing dressing assistance to real people with physical disabilities. Physics Simulation for Assistive Robotics: While robotic caregivers may present an opportunity to improve the quality of life for people who require daily assistance, conducting this type of research presents several challenges, including high costs, slow data collection, and risks of physical interaction between people and robots. We have recently introduced Assistive Gym, the first open source physics-based simulation framework for modeling physical human-robot interaction and robotic assistance. We demonstrate how physics simulation can open up entirely new research directions and opportunities within physical human-robot interaction. This includes training versatile assistive robots, developing control algorithms towards common sense reasoning, constructing baselines and benchmarks for robotic caregiving, and investigating generalization of physical human-robot interaction from a number of angles, including human motion, preferences, and variation in human body shape and impairments. Finally, we show how virtual reality (VR) can help bridge the reality gap by bringing real people into physics simulation to interact with and receive assistance from virtual robotic caregivers.Ph.D

    Human Machine Interaction

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    In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

    Technology for large space systems: A bibliography with indexes (supplement 11)

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    This bibliography contains 539 abstracts of reports, articles and other documents introduced into the NASA scientific and technical information system between January 1, 1984 and December 31, 1984. Abstracts are arranged in the following categories: systems; analysis and design techniques; structural concepts; structural and thermal analysis; structural dynamics and control; electronics; advanced materials; assembly concepts; propulsion; and miscellaneous. Subject, personal author, corporate source, contract number, report number, and accession number indexes are listed

    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

    Personalized data analytics for internet-of-things-based health monitoring

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    The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

    Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

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    This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

    Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

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    Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018
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