A Hybrid Physical and Data-drivApproach to Motion Prediction and Control in Human-Robot Collaboration PDF Download

Author: Min Wu
Publisher: Logos Verlag Berlin GmbH
ISBN: 383255484X
Category : Technology & Engineering
Languages : en
Pages : 212

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Book Description
In recent years, researchers have achieved great success in guaranteeing safety in human-robot interaction, yielding a new generation of robots that can work with humans in close proximity, known as collaborative robots (cobots). However, due to the lack of ability to understand and coordinate with their human partners, the ``co'' in most cobots still refers to ``coexistence'' rather than ``collaboration''. This thesis aims to develop an adaptive learning and control framework with a novel physical and data-driven approach towards a real collaborative robot. The first part focuses on online human motion prediction. A comprehensive study on various motion prediction techniques is presented, including their scope of application, accuracy in different time scales, and implementation complexity. Based on this study, a hybrid approach that combines physically well-understood models with data-driven learning techniques is proposed and validated through a motion data set. The second part addresses interaction control in human-robot collaboration. An adaptive impedance control scheme with human reference estimation is presented. Reinforcement learning is used to find optimal control parameters to minimize a task-orient cost function without fully knowing the system dynamic. The proposed framework is experimentally validated through two benchmark applications for human-robot collaboration: object handover and cooperative object handling. Results show that the robot can provide reliable online human motion prediction, react early to human motion variation, make proactive contributions to physical collaborations, and behave compliantly in response to human forces.

Author: Luka Peternel
Publisher: Frontiers Media SA
ISBN: 2889633128
Category :
Languages : en
Pages : 229

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Book Description
In the past years there has been considerable effort to move robots from industrial environments to our daily lives where they can collaborate and interact with humans to improve our life quality. One of the key challenges in this direction is to make a suitable robot control system that can adapt to humans and interactively learn from humans to facilitate the efficient and safe co-existence of the two. The applications of such robotic systems include: service robotics and physical human-robot collaboration, assistive and rehabilitation robotics, semi-autonomous cars, etc. To achieve the goal of integrating robotic systems into these applications, several important research directions must be explored. One such direction is the study of skill transfer, where a human operator’s skilled executions are used to obtain an autonomous controller. Another important direction is shared control, where a robotic controller and humans control the same body, tool, mechanism, car, etc. Shared control, in turn invokes very rich research questions such as co-adaptation between the human and the robot, where the two agents can benefit from each other’s skills or must adapt to each other’s behavior to achieve effective cooperative task executions. The aim of this Research Topic is to help bridge the gap between the state-of-the-art and above-mentioned goals through novel multidisciplinary approaches in human-in-the-loop robot control and learning.

Author: Zhaojie Ju
Publisher: Springer Nature
ISBN: 3030299333
Category : Technology & Engineering
Languages : en
Pages : 556

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Book Description
This book highlights the latest research in computational intelligence and its applications. It covers both conventional and trending approaches in individual chapters on Fuzzy Systems, Intelligence in Robotics, Deep Learning Approaches, Optimization and Classification, Detection, Inference and Prediction, Hybrid Methods, Emerging Intelligence, Intelligent Health Care, and Engineering Data- and Model-Driven Applications. All chapters are based on peer-reviewed contributions presented at the 19th Annual UK Workshop on Computational Intelligence, held in Portsmouth, UK, on 4–6 September 2019. The book offers a valuable reference guide for readers with expertise in computational intelligence or who are seeking a comprehensive and timely review of the latest trends in computational intelligence. Special emphasis is placed on novel methods and their use in a wide range of application areas, updating both academics and professionals on the state of the art.

Author: Przemyslaw Andrzej Lasota
Publisher:
ISBN:
Category :
Languages : en
Pages : 188

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Book Description
From robotic co-workers in factories to assistive robots in homes, human-robot interaction (HRI) has the potential to revolutionize a large array of domains by enabling robotic assistance where it was previously not possible. Introducing robots into human-occupied domains, however, requires strong consideration for the safety and efficiency of the interaction. One particularly effective method of supporting safe an efficient human-robot interaction is through the use of human motion prediction. By predicting where a person might reach or walk toward in the upcoming moments, a robot can adjust its motions to proactively resolve motion conflicts and avoid impeding the person's movements. Current approaches to human motion prediction, however, often lack the robustness required for real-world deployment. Many methods are designed for predicting specific types of tasks and motions, and do not necessarily generalize well to other domains. It is also possible that no single predictor is suitable for predicting motion in a given scenario, and that multiple predictors are needed. Due to these drawbacks, without expert knowledge in the field of human motion prediction, it is difficult to deploy prediction on real robotic systems. Another key limitation of current human motion prediction approaches lies in deficiencies in partial trajectory alignment. Alignment of partially executed motions to a representative trajectory for a motion is a key enabling technology for many goal-based prediction methods. Current approaches of partial trajectory alignment, however, do not provide satisfactory alignments for many real-world trajectories. Specifically, due to reliance on Euclidean distance metrics, overlapping trajectory regions and temporary stops lead to large alignment errors. In this thesis, I introduce two frameworks designed to improve the robustness of human motion prediction in order to facilitate its use for safe and efficient human-robot interaction. First, I introduce the Multiple-Predictor System (MPS), a datadriven approach that uses given task and motion data in order to synthesize a high performing predictor by automatically identifying informative prediction features and combining the strengths of complementary prediction methods. With the use of three distinct human motion datasets, I show that using the MPS leads to lower prediction error in a variety of HRI scenarios, and allows for accurate prediction for a range of time horizons. Second, in order to address the drawbacks of prior alignment techniques, I introduce the Bayesian ESTimator for Partial Trajectory Alignment (BEST-PTA). This Bayesian estimation framework uses a combination of optimization, supervised learning, and unsupervised learning components that are trained and synthesized based on a given set of example trajectories. Through an evaluation on three human motion datasets, I show that BEST-PTA reduces alignment error when compared to state-of-the-art baselines. Furthermore, I demonstrate that this improved alignment reduces human motion prediction error. Lastly, in order to assess the utility of the developed methods for improving safety and efficiency in HRI, I introduce an integrated framework combining prediction with robot planning in time. I describe an implementation and evaluation of this framework on a real physical system. Through this demonstration, I show that the developed approach leads to automatically derived adaptive robot behavior. I show that the developed framework leads to improvements in quantitative metrics of safety and efficiency with the use of a simulated evaluation.

Author: Tan Sy Nguyen
Publisher:
ISBN:
Category : Human-robot interaction
Languages : en
Pages : 0

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Book Description
This thesis investigates motion control methods and physical human robot interaction (pHRI) control strategies for two robotic systems, namely a kinematically redundant hybrid parallel robot (KRHPR) and a macro-mini system. The kinematic analysis, the dynamic modelling, as well as the control methods proposed in the thesis can be generalized for a class of robots with similar architecture. The thesis firstly introduces a novel kinematically redundant (6+3)-degree-of-freedom (DoF) spatial hybrid parallel robot with revolute actuators. The kinematic equations are developed and the singularities are examined. The translational and rotational workspace of the robot is then analysed. Also, a new mechanism is introduced to operate a gripper using the redundant DoFs. Thanks to the backdrivability of the robot, a controller - which can flexibly switch between two modes: position control and interaction control - is developed to demonstrate the potential use of this robot for physical interaction without using a force/torque sensor or joint torque sensors. Secondly, the motion control problem is investigated for a class of spatial kinematically redundant hybrid parallel robots. The kinematics are recalled and the dynamics are analysed. Based on this analysis, a proposed method referred to as hybrid control algorithm is proposed. It combines a simplified computed-torque controller, that operates in the joint space, with a Cartesian compensation, that operates in the task space of the robot. The stability of this approach is verified. Then, experiments are carried out on two example architectures. The results are examined and compared to those obtained with other methods to validate the effectiveness of the proposed approach. The motion control of a macro-mini system, which combines the hybrid parallel robot and a gantry system, is then investigated. The kinematics and the dynamics of the combined system are mainly analysed in the task space since it can be assumed that the position of the macro and the mini is stably determined by their own controllers. Motion control methods, namely mid-ranging control and Model Predictive Control, are generalized and adapted. Also, the combination of PI and the redundancy resolution is proposed. Each control method is implemented and used to perform the same trajectory. Afterwards, the control error is determined in order to compare the performance of the different methods. The physical human robot interaction is then studied for each of the robotic platforms mentioned above. On the KRHPR, a stiffness-damping control is specifically developed for pHRI applications. On the macro-mini system, the interaction method is also examined. The stability and the operational performance is analysed in detail. Experiments involving pHRI are then conducted and some demonstrations of potential applications are also presented. Finally, the conclusion summarizes the results obtained and discusses current limitations and potential future work.

Author: Adam Spiers
Publisher: Springer
ISBN: 3319301608
Category : Technology & Engineering
Languages : en
Pages : 286

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Book Description
This book investigates a biologically inspired method of robot arm control, developed with the objective of synthesising human-like motion dynamically, using nonlinear, robust and adaptive control techniques in practical robot systems. The control method caters to a rising interest in humanoid robots and the need for appropriate control schemes to match these systems. Unlike the classic kinematic schemes used in industrial manipulators, the dynamic approaches proposed here promote human-like motion with better exploitation of the robot’s physical structure. This also benefits human-robot interaction. The control schemes proposed in this book are inspired by a wealth of human-motion literature that indicates the drivers of motion to be dynamic, model-based and optimal. Such considerations lend themselves nicely to achievement via nonlinear control techniques without the necessity for extensive and complex biological models. The operational-space method of robot control forms the basis of many of the techniques investigated in this book. The method includes attractive features such as the decoupling of motion into task and posture components. Various developments are made in each of these elements. Simple cost functions inspired by biomechanical “effort” and “discomfort” generate realistic posture motion. Sliding-mode techniques overcome robustness shortcomings for practical implementation. Arm compliance is achieved via a method of model-free adaptive control that also deals with actuator saturation via anti-windup compensation. A neural-network-centered learning-by-observation scheme generates new task motions, based on motion-capture data recorded from human volunteers. In other parts of the book, motion capture is used to test theories of human movement. All developed controllers are applied to the reaching motion of a humanoid robot arm and are demonstrated to be practically realisable. This book is designed to be of interest to those wishing to achieve dynamics-based human-like robot-arm motion in academic research, advanced study or certain industrial environments. The book provides motivations, extensive reviews, research results and detailed explanations. It is not only suited to practising control engineers, but also applicable for general roboticists who wish to develop control systems expertise in this area.

Author: Yuquan Leng
Publisher: Frontiers Media SA
ISBN: 2832549853
Category : Science
Languages : en
Pages : 134

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Book Description
With the progress and development of human-robot systems, the coordination among humans, robots, and environments has become increasingly sophisticated. In this Research Topic, we focus on an important field in robotics and automation disciplines, which is commonly defined as behavior-assistant robots. The scope includes but is not limited to: (1) rehabilitation assistive devices, such as rigid/soft exoskeletons, prosthetic systems, orthoses, and intelligent wheelchairs; (2) intelligent medical systems, such as endoscopic robots, surgical robots, and the navigation systems; (3) industrial application devices, such as collaborative manipulators, load-bearing exoskeletons, supernumerary robotic limbs; (4) intelligent domestic devices, such as mobile robots, elderly-care robots, walking-aids robots and so on. The emergence of robot-assisted daily behaviors, based on aforementioned devices, is gradually becoming part of our social lives, which can improve weak motor abilities, enhance physical functionalities, and enable various other benefits.

Author: Yanhao He
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832554408
Category : Technology & Engineering
Languages : en
Pages : 188

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Book Description
Since the manipulation tasks for robotic systems become more and more complicated, multi-robot cooperation has been attracting much attention recently. Furthermore, under the trend of human-robot co-existence, collision-free motion control is now also desired on multi-robot groups. This dissertation aims to design a novel distributed optimal control framework to deal with multi-robot cooperative manipulation of rigid objects in dynamic environments. Besides object transportation, the control scheme also tackles obstacle avoidance, joint-space performance optimisation and internal force suppression. The proposed control framework has a two-layer structure, with a distributed optimisation algorithm in the kinematic layer for generating proper joint configuration references, followed by a robot motion controller in the dynamic control layer to fulfil the reference. An indirect and a direct distributed optimisation method are developed for the kinematic layer, both of which are computationally and communicationally efficient. In the dynamic control layer, impedance control is employed for safe physical interaction. As another highlight, abundant experiments carried out on a multi-arm test bench have demonstrated the effectiveness of the presented control schemes under various environmental and task settings. The recorded computation time shows the applicability of the control framework in practice.

Author: Nicoletta Noceti
Publisher: Springer Nature
ISBN: 3030467325
Category : Computers
Languages : en
Pages : 351

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Book Description
The new frontiers of robotics research foresee future scenarios where artificial agents will leave the laboratory to progressively take part in the activities of our daily life. This will require robots to have very sophisticated perceptual and action skills in many intelligence-demanding applications, with particular reference to the ability to seamlessly interact with humans. It will be crucial for the next generation of robots to understand their human partners and at the same time to be intuitively understood by them. In this context, a deep understanding of human motion is essential for robotics applications, where the ability to detect, represent and recognize human dynamics and the capability for generating appropriate movements in response sets the scene for higher-level tasks. This book provides a comprehensive overview of this challenging research field, closing the loop between perception and action, and between human-studies and robotics. The book is organized in three main parts. The first part focuses on human motion perception, with contributions analyzing the neural substrates of human action understanding, how perception is influenced by motor control, and how it develops over time and is exploited in social contexts. The second part considers motion perception from the computational perspective, providing perspectives on cutting-edge solutions available from the Computer Vision and Machine Learning research fields, addressing higher-level perceptual tasks. Finally, the third part takes into account the implications for robotics, with chapters on how motor control is achieved in the latest generation of artificial agents and how such technologies have been exploited to favor human-robot interaction. This book considers the complete human-robot cycle, from an examination of how humans perceive motion and act in the world, to models for motion perception and control in artificial agents. In this respect, the book will provide insights into the perception and action loop in humans and machines, joining together aspects that are often addressed in independent investigations. As a consequence, this book positions itself in a field at the intersection of such different disciplines as Robotics, Neuroscience, Cognitive Science, Psychology, Computer Vision, and Machine Learning. By bridging these different research domains, the book offers a common reference point for researchers interested in human motion for different applications and from different standpoints, spanning Neuroscience, Human Motor Control, Robotics, Human-Robot Interaction, Computer Vision and Machine Learning. Chapter 'The Importance of the Affective Component of Movement in Action Understanding' of this book is available open access under a CC BY 4.0 license at link.springer.com.

Author: Sylvain Calinon
Publisher: EPFL Press
ISBN: 9781439808672
Category : Computers
Languages : en
Pages : 248

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Book Description
Recent advances in RbD have identified a number of key issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This book focuses on the two generic questions of what to imitate and how to imitate and proposes active teaching methods.