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Author: Hao Zhang (Researcher in robotics)
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 229
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Book Description
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.
Author: Hao Zhang (Researcher in robotics)
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 229
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Book Description
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.
Author: Pål Liljebäck
Publisher: Springer Science & Business Media
ISBN: 1447129962
Category : Technology & Engineering
Languages : en
Pages : 317
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Book Description
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.
Author: Fahad Mirza
Publisher:
ISBN:
Category :
Languages : en
Pages : 98
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Book Description
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.
Author: Gordon Cheng
Publisher: CRC Press
ISBN: 1420093673
Category : Medical
Languages : en
Pages : 288
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Book Description
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
Author: Francisco Ferrer Sales
Publisher: University of Coimbra
ISBN:
Category :
Languages : en
Pages : 49
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Book Description
Author: Timm Linder
Publisher:
ISBN:
Category : Optical radar
Languages : en
Pages :
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Book Description
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
Author: Jesus Hamilton Ortiz
Publisher: BoD – Books on Demand
ISBN: 1839687398
Category : Technology & Engineering
Languages : en
Pages : 184
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Book Description
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.
Author: Bin Fang
Publisher: Springer Nature
ISBN: 9811551243
Category : Technology & Engineering
Languages : en
Pages : 219
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Book Description
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.
Author: Hazem Wannous
Publisher: Springer
ISBN: 331991863X
Category : Computers
Languages : en
Pages : 129
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Book Description
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.
Author: Benjamin Shih
Publisher:
ISBN:
Category :
Languages : en
Pages : 184
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Book Description
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.
