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3d Robotic Sensing Of People
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Book Synopsis 3D Robotic Sensing of People by : Hao Zhang (Researcher in robotics)
Download or read book 3D Robotic Sensing of People written by Hao Zhang (Researcher in robotics) and published by . This book was released on 2014 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives. As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications. This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new "CoDe4D" features that incorporate both color and depth information, a new "SOD" descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics.
Download or read book Snake Robots written by Pål Liljebäck and published by Springer Science & Business Media. This book was released on 2012-06-13 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Snake Robots is a novel treatment of theoretical and practical topics related to snake robots: robotic mechanisms designed to move like biological snakes and able to operate in challenging environments in which human presence is either undesirable or impossible. Future applications of such robots include search and rescue, inspection and maintenance, and subsea operations. Locomotion in unstructured environments is a focus for this book. The text targets the disparate muddle of approaches to modelling, development and control of snake robots in current literature, giving a unified presentation of recent research results on snake robot locomotion to increase the reader’s basic understanding of these mechanisms and their motion dynamics and clarify the state of the art in the field. The book is a complete treatment of snake robotics, with topics ranging from mathematical modelling techniques, through mechatronic design and implementation, to control design strategies. The development of two snake robots is described and both are used to provide experimental validation of many of the theoretical results. Snake Robots is written in a clear and easily understandable manner which makes the material accessible by specialists in the field and non-experts alike. Numerous illustrative figures and images help readers to visualize the material. The book is particularly useful to new researchers taking on a topic related to snake robots because it provides an extensive overview of the snake robot literature and also represents a suitable starting point for research in this area.
Book Synopsis Human Robot Interaction with 3D-printed Whole Body Robotic Skin by : Fahad Mirza
Download or read book Human Robot Interaction with 3D-printed Whole Body Robotic Skin written by Fahad Mirza and published by . This book was released on 2016 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents work of a newly developed pressure sensor for robot skin, from fabrication to application. Robot skin, similar to human skin, is meant to have several sensing capabilities including pressure, temperature etc. As the conventional semiconductor manufacturing process is not adequate for multi-modal sensors, a new process called Electro-Hydro-Dynamic Printing has been explored in this work. A calibration technique was implemented along with printing parameter fixation by trial and error. A testing methodology was proposed to test these sensors and several commercial robots were used to conduct pHRI experiments with pressure sensing capability.
Book Synopsis 3Dnav - 3D Navigation, Exploration and Detection with a Mobile Robot by : Francisco Ferrer Sales
Download or read book 3Dnav - 3D Navigation, Exploration and Detection with a Mobile Robot written by Francisco Ferrer Sales and published by University of Coimbra. This book was released on 2013-12-01 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multi-modal Human Detection, Tracking and Analysis for Robots in Crowded Environments by : Timm Linder
Download or read book Multi-modal Human Detection, Tracking and Analysis for Robots in Crowded Environments written by Timm Linder and published by . This book was released on 2020* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The ability to perceive humans in their surroundings is a key ingredient for robots that operate in environments shared with humans, for example in consumer, industrial and automotive applications - such as a service robot for person guidance, an autonomous forklift in a warehouse, or a self-driving vehicle. This thesis deals with the problem of robustly detecting and tracking humans and recognizing their attributes in challenging environments in real-time, from the egocentric perspective of a computationally constrained mobile robot equipped with multiple sensing modalities. To address this problem, we examine both classical, model-based approaches and deep learning-based methods, and evaluate them on novel datasets as well as during real-world deployments on different mobile robot platforms in populated indoor scenarios. We start this thesis with the question if complex data association methods are suitable for tracking groups of people in general, and in crowded environments in particular. To this end, we address the problem of joint individual-group tracking using learned pairwise social relations in RGB-D by extending an existing multi-model multi-hypothesis tracking method with a mechanism to maintain consistent group identities. In qualitative experiments on a novel dataset from a pedestrian zone, we achieve good real-time tracking performance for varying group sizes with few identifier switches. We apply the method to socially-aware navigation use-cases and present further experiments on simulated data in a more crowded environment, where we examine limitations of the hypothesis-oriented MHT approach under real-time constraints. We then take a step back from group tracking and investigate the problem of tracking individual humans in crowded scenes using a mobile platform with a multi-modal sensor setup. Here, we first introduce a computationally very efficient tracking baseline: Using a relatively cheap set of extensions from the target tracking community to systematically tackle shortcomings of current systems, we attempt to improve robustness without resorting to more complex data association methods. After automated hyperparameter optimization, we compare our method systematically under different detector combinations to a hypothesis-oriented MHT, a track-oriented MDL tracker, and different NN variants on two novel datasets. We find that our efficient baseline method outperforms all other evaluated methods on the MOTA metric across all settings. Our key finding is that detector performance is the single, most influential factor affecting tracking performance which goes far beyond the impact of the chosen tracking algorithm. Therefore, we focus our subsequent research on the detection task. One insight we gain from initial experiments is that recent CNN-based detectors perform well on 2D image-based detection, but this does not easily translate into robust localization in 3D world space. To deal with this, we develop a fast CNN-based one-stage detector that benefits from complementary RGB and depth image data and regresses 3D human centroids in an end-to-end fashion. We show that we can efficiently learn their 3D localization from a highly randomized RGB-D dataset that has been synthetically generated using a modern game engine, while exploiting existing real-world 2D object detection datasets to pretrain the detection task. The resulting method outperforms several state-of-the-art baselines, including a 3D articulated human pose estimation approach. For 2D laser-based leg detection, we examine several classical model-based detection approaches as well as a CNN-based method that can be improved by observing human leg movement over a sequence of frames, while conducting experiments on a large-scale dataset from an elderly care facility. We then consider also methods for human detection in 3D lidar and RGB-D, and quantitatively compare detection performance across all three sensor modalities on two novel sequences in a challenging intralogistics scenario. This provides us with interesting insights on their strengths, weaknesses and generalization capabilities: In particular, we learn that the 3D lidar methods, which have been trained on available autonomous driving datasets, do not seem to transfer well to our application domain, where large-scale training datasets are not available; we observe problems especially in narrow and cluttered spaces. This indicates the need for more large-scale, domain-specific datasets and benchmarks in robotics, as well as methods that can generalize better with limited amounts of training data. We finally take a closer look at humans in order to recognize their individual attributes. To this end, we extend an efficient tessellation-boosting method to recognize human attributes from RGB-D point clouds. The method achieves over 300 Hz without GPU, and can compete with computationally more complex deep learning-based methods on our novel attributes dataset. Throughout this thesis, we acquired, annotated and analyzed several novel datasets in challenging environments, like a pedestrian zone, a crowded airport terminal, and intralogistics warehouses. The presented methods have been extensively validated "in the wild" to show their general applicability. To combine the methods, we propose a unified, multi-modal, ROS-based human detection and tracking framework that facilitates their deployment and evaluation. Due to its modular design with reusable interfaces and software components, we were able to deploy it on close to a dozen different robot platforms. In particular, we gathered experiences with a socially-aware mobile service robot for person guidance that we deployed inside a crowded airport terminal. Here, system contributions have been made that go beyond human detection, tracking and analysis and touch the topics of sensor calibration, human-robot interaction, distributed software architecture and practical safety considerations. We share previously unpublished lessons learned during this ambitious project, which we hope will benefit future research in this area
Book Synopsis Wearable Technology for Robotic Manipulation and Learning by : Bin Fang
Download or read book Wearable Technology for Robotic Manipulation and Learning written by Bin Fang and published by Springer Nature. This book was released on 2020-10-06 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, millions of people, with varying backgrounds and levels of technical expertise, will have to effectively interact with robotic technologies on a daily basis. This means it will have to be possible to modify robot behavior without explicitly writing code, but instead via a small number of wearable devices or visual demonstrations. At the same time, robots will need to infer and predict humans’ intentions and internal objectives on the basis of past interactions in order to provide assistance before it is explicitly requested; this is the basis of imitation learning for robotics. This book introduces readers to robotic imitation learning based on human demonstration with wearable devices. It presents an advanced calibration method for wearable sensors and fusion approaches under the Kalman filter framework, as well as a novel wearable device for capturing gestures and other motions. Furthermore it describes the wearable-device-based and vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic knowledge of machine learning, and for researchers interested in wearable computing and robotic learning.
Book Synopsis Understanding Human Activities Through 3D Sensors by : Hazem Wannous
Download or read book Understanding Human Activities Through 3D Sensors written by Hazem Wannous and published by Springer. This book was released on 2018-05-15 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the Second International Workshop on Understanding Human Activities through 3D Sensors, UHA3DS 2016, that was held in conjunction with the 23rd International Conference on Pattern Recognition, ICPR 2016, held in Cancun, Mexico, in December 2016. The 9 revised full papers were carefully reviewed and selected from 12 submissions. The papers are organized in topical sections on Behavior Analysis, Human Motion Recognition, and Application Datasets.
Book Synopsis Informative Touch for Intelligent Soft Robots by : Benjamin Shih
Download or read book Informative Touch for Intelligent Soft Robots written by Benjamin Shih and published by . This book was released on 2020 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: As robots grow increasingly prevalent in real-world environments, sensory systems capable of sensing complex deformations and environmental interactions are needed for robust control. Soft robotics has emerged as a field of study that seeks to replace rigid components in traditional robots with materials that are compliant. It has garnered interest for real-world applications due to intrinsic safety embedded at the material level, deformable materials capable of shape and behavioral changes, and conformable physical contact for manipulation.-Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multi-modal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This dissertation describes the design, fabrication, and data processing of soft, tactile sensor skins, with the ultimate goal of enhancing future collaborative robots that will work alongside and physically interact with people with a human-like sense of touch. This thesis focuses on how the integration of soft sensor skins and machine learning enables soft robots to perceive physical interaction for complex haptic tasks. Part 1 on Soft Sensors (Chapters 2, 3, and 4) presents the design and fabrication of various types of soft strain sensors, using compliant materials such as silicones and polymers. Fabrication methods include soft lithography and 3D printing. Performance of the sensors are characterized and modeled. Part 2 on Soft Robot Perception (Chapters 5 and 6) describes how machine learning can be used to augment the performance of soft sensors and actuators. The method demonstrates how recurrent neural networks can be used for graceful degradation and learned perception of external contacts and forces despite not having a priori information about the individual sensors. Part 3 on Social Touch for Physical Human-Robot Interaction (Chapters 7 and 8) analyzes the use of the liquid metal sensors as robotic skins for the classification of affective (social) touch and builds towards the development of a framework for representing physical contact, for use at the human-robot interaction layer of abstraction. In recent years, the concept of soft-bodied robots has rapidly grown in popularity. Researchers have developed many interesting forms of actuation that more closely mimic the functionality and capabilities found in nature. The next step for the field is to develop biologically-inspired tactile sensing for soft-bodied robots that can safely interact with and explore their environments. In the short term, the field can focus on deployable, high-resolution sensor skins, algorithms for processing the dense sensor information, and reliable feedback control for soft robots. Building upon the fundamental work presented in this dissertation, the future consists of robots that can touch and feel with the sensitivity and perception of natural systems.
Book Synopsis Humanoid Robotics and Neuroscience by : Gordon Cheng
Download or read book Humanoid Robotics and Neuroscience written by Gordon Cheng and published by CRC Press. This book was released on 2014-12-19 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humanoid robots are highly sophisticated machines equipped with human-like sensory and motor capabilities. Today we are on the verge of a new era of rapid transformations in both science and engineering-one that brings together technological advancements in a way that will accelerate both neuroscience and robotics. Humanoid Robotics and Neuroscienc
Book Synopsis Human Motion Sensing and Recognition by : Honghai Liu
Download or read book Human Motion Sensing and Recognition written by Honghai Liu and published by Springer. This book was released on 2017-05-11 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the latest exciting advances in human motion sensing and recognition, from the theoretical development of fuzzy approaches to their applications. The topics covered include human motion recognition in 2D and 3D, hand motion analysis with contact sensors, and vision-based view-invariant motion recognition, especially from the perspective of Fuzzy Qualitative techniques. With the rapid development of technologies in microelectronics, computers, networks, and robotics over the last decade, increasing attention has been focused on human motion sensing and recognition in many emerging and active disciplines where human motions need to be automatically tracked, analyzed or understood, such as smart surveillance, intelligent human-computer interaction, robot motion learning, and interactive gaming. Current challenges mainly stem from the dynamic environment, data multi-modality, uncertain sensory information, and real-time issues. These techniques are shown to effectively address the above challenges by bridging the gap between symbolic cognitive functions and numerical sensing & control tasks in intelligent systems. The book not only serves as a valuable reference source for researchers and professionals in the fields of computer vision and robotics, but will also benefit practitioners and graduates/postgraduates seeking advanced information on fuzzy techniques and their applications in motion analysis.
Book Synopsis Human Behavior Understanding in Networked Sensing by : Paolo Spagnolo
Download or read book Human Behavior Understanding in Networked Sensing written by Paolo Spagnolo and published by Springer. This book was released on 2014-11-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of both the technical challenges in sensor network development, and the real-world applications of distributed sensing. Important aspects of distributed computing in large-scale networked sensor systems are analyzed in the context of human behavior understanding, including topics on systems design tools and techniques. Additionally, the book examines a varied range of applications. Features: contains valuable contributions from an international selection of leading experts in the field; presents a high-level introduction to the aims and motivations underpinning distributed sensing; describes decision-making algorithms in the presence of complex sensor networks; provides a detailed analysis of the design, implementation, and development of a distributed network of homogeneous or heterogeneous sensors; reviews the application of distributed sensing to human behavior understanding and autonomous intelligent vehicles; includes a helpful glossary and a list of acronyms.
Book Synopsis Deep Learning for Robot Perception and Cognition by : Alexandros Iosifidis
Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Book Synopsis Tactile Sensors for Robotic Applications by : Salvatore Pirozzi
Download or read book Tactile Sensors for Robotic Applications written by Salvatore Pirozzi and published by MDPI. This book was released on 2021-03-17 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers different aspects: - Innovative technologies for tactile sensors development - Tactile data interpretation for control purposes - Alternative sensing technologies - Multi-sensor systems for grasping and manipulation - Sensing solutions for impaired people
Book Synopsis Collaborative and Humanoid Robots by : Jesus Hamilton Ortiz
Download or read book Collaborative and Humanoid Robots written by Jesus Hamilton Ortiz and published by BoD – Books on Demand. This book was released on 2021-09-29 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative and Humanoid Robots guides readers through the fundamentals and state-of-the-art concepts and future expectations of robotics. It showcases interesting research topics on robots and cobots by researchers, industry practitioners, and academics. Divided into two sections on “Collaborative Robots” and “Humanoid Robots,” this book includes surveys of recent publications that investigative the interaction between humanoid robots and humans; safe adaptive trajectory tracking control of robots; 3D printed, self-learning robots; robot trajectory, guidance, and control; social robots; Tiny Blind assistive humanoid robots; and more.
Book Synopsis Investigating the Use of Microsoft Kinect 3D Imaging for Robotic Person Following by :
Download or read book Investigating the Use of Microsoft Kinect 3D Imaging for Robotic Person Following written by and published by . This book was released on 2014 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis evaluated overall effectiveness of utilizing Microsoft Kinect's marketed 3D tracking capabilities for applications of person detection and following. The advantage Kinect provided over traditional CCD image sensors was its marketed ability in reliable person detection without need for prior calibration. In addition, Kinect person detection was non-color based, proving useful in scenarios of similarity between a person's clothing color and their surrounding. The Kinect was given pan/tilt motion through servo control and mounted on the iRobot Create mobile robotic platform. Detection and extraction of skeleton joint position utilized SimpleOpenNI framework in conjunction with PrimeSense NITE library and implemented over Processing IDE. Mounted onto the robot was a laptop computer that processed extracted skeleton data into movement control commands to be sent via serial protocol. An Arduino microcontroller processed received serial movement commands into PWM for pan/tilt servo control. Movement commands sent via serial to the iRobot Create detailed speed and direction for physical navigation through an environment. The person following robot was tested on different types of terrain and under varying lighting conditions. Evaluation of lighting conditions revealed Kinect person detection reliability was ideally suited for low indoor lighting scenarios with little to no illumination sources. In outdoor lighting scenarios of bright daytime sunlight, the Kinect was incapable of person detection. Evaluating different types of terrain revealed person detection faired well on soft shock-absorbent terrain while performing poor on rough uneven terrain. It was concluded that even under ideal lighting conditions and optimal terrain, the Kinect alone exhibited difficulties for applications of person following.
Book Synopsis Multi-Component Force Sensing Systems by : Qiaokang Liang
Download or read book Multi-Component Force Sensing Systems written by Qiaokang Liang and published by CRC Press. This book was released on 2021-02-28 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-Component Force Sensing Systems focuses on the design, development, decoupling, and applications of multi-component force sensing systems. Force and moment information can be used as feedback to form an automatic control system to accomplish efficient manipulation. The origins of force measurement and control can be traced back to the late 1970s. Since then, multi-component F/M (force/moment) sensing systems have been widely known and intensively studied. In the past few years, force measurement practices have been significantly affected by new tools (such as digital force gauges, virtual instrumentation, high speed data acquisition systems, etc.) as well as sophisticated measurement methods such as mechano-magnetic, mechano-optical, etc. However, this is the first book to provide an overview of the topic. It will be a useful reference for students in physics and engineering working with robotic sensing systems and robotic systems, in addition to researchers and those working within industry. This work was supported in part by the National Nature Science Foundation of China (NSFC 62073129 and 61673163). Features: • Explores the development of force/torque sensing systems • Provides real applications of the multi-component force/torque sensing systems • Contains executable code for decoupling algorithms About the Author: Qiaokang Liang is an Associate Professor with the College of Electrical and Information Engineering, Hunan University. He is currently the vice director of the Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing and serving as the assistant director of the National Engineering Laboratory for Robot Vision Perception and Control. He received his Ph.D. degree in control science and engineering from the University of Science and Technology of China, Hefei, China, in 2011. His research interests include robotics and mechatronics, biomimetic sensing, advanced robot technology, and human–computer interaction.
Book Synopsis Roadmap for the Integration of Sensing Systems in Robotics-Based Manufacturing of Industrialized Construction by : Kush Shah
Download or read book Roadmap for the Integration of Sensing Systems in Robotics-Based Manufacturing of Industrialized Construction written by Kush Shah and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: human workers and robots equipped with advanced sensing systems and artificial intelligence. Those sensing technologies will allow robots to avoid collision with human employees by actively monitoring human behaviors, postures, and motions and reactively predicting human reactions. As a result, developing a road map for incorporating sensing systems into uncertain, human-centric manufacture of the industrialized building is the focus of this thesis. This study presents a systematic review of state-of-the-art sensing technologies (such as 2D and 3D cameras and LiDAR) and algorithms and algorithms in order to select the best-suited sensing systems for human-robot collaboration (HRC) in industrialized construction. The proposed evaluation will take into account key factors that represent the unique characteristics of construction materials, human workers, robotic workstations, and human and robot tasks in industrialized construction. A task-driven guideline will then be developed to help construction professionals make informed decisions regarding the integration of sensing systems in their respective, real-world manufacturing lines. This research is the first step that sheds the light on the current restrictions and limitations of interactions between people and industrial robots. The developed framework and guidelines will contribute to facilitating safe human-robot collaboration in unfenced environments.
