[PDF] Towards A Reactive Motion Generation On Exteroceptive Feedback For Generalized Locomotion Of Humanoid Robots eBook

Author : Kevin Giraud--Esclasse
Publisher :
Page : 127 pages
File Size : 45,35 MB
Release : 2019
Category :
ISBN :

GET BOOK

Humanoid robots need exteroceptive sensors such as cameras to perceive their environment and fulfill tasks in it. This thesis deals with the integration of visual information for robot control. More specifically, in order to realize a behavior, visual data are needed to drive the robot's whole body trajectory generator either on flat ground or in multicontact. We first recall how a humanoid robot is controlled for a locomotion task, starting from the reference positions sent to the planner that computes sequence of contacts used to generate centroidal trajectory. This one is injected in a Whole body trajectory generator that provides joint trajectories to be sent to the robot through a stabilizer. Depending on the type of data given by the vision block algorithm (considered as an input during this thesis), visual loops can be made at different levels of the previous pipeline. The objectives were to use on the shelf visual blocks outputs to provide experimental results bas! ed on former blocks integration. We treated first motion capture data as high level information giving them to a Pattern Generator (PG) in charge of computing steps for the robot. One goal was to realize integrative tests for the Koroibot challenge by connecting motions created to pass obstacles like stairs or a beam. Results on the robot were not satisfying due to poor motion repeatability. The fault was due to the assumptions used between model and real robot or external phenomena like mechanical wear and stabilizer effects. To have better quantification of the repeatability and reliability of the walking algorithms on the HRP2 robot, we realized experiments in collaboration with the French Metrology and Tests Laboratory (LNE). Our collaborators provided test plateforms like climatic room, adjustable angle slope and horizontal oscillations floor to measure Key Performance Indicators (KPI). Finally, to reach multicontact motions based on vision output, 2D features projecte! d on image camera plan have been expressed in a promising opti! mal control solver called DDP (Differential Dynamic Programming). It allows to take into account non-linearities of the features projection directly in the whole body trajectory generator. Simulations for locomotion motions with multicontact using simulated visual features were provided with the robot TALOS. The remaining main issue lies in the inequality constraints that are not implemented yet in the DDP solver core. In that last part, all the elements of the pipeline previously exposed are used together : from the pose specification to the motion passed on simulation that uses stabilization module before beeing sent to the actuator commands.

Author : Kensuke Harada
Publisher : Springer Science & Business Media
Page : 320 pages
File Size : 26,15 MB
Release : 2010-08-12
Category : Technology & Engineering
ISBN : 1849962200

GET BOOK

Research on humanoid robots has been mostly with the aim of developing robots that can replace humans in the performance of certain tasks. Motion planning for these robots can be quite difficult, due to their complex kinematics, dynamics and environment. It is consequently one of the key research topics in humanoid robotics research and the last few years have witnessed considerable progress in the field. Motion Planning for Humanoid Robots surveys the remarkable recent advancement in both the theoretical and the practical aspects of humanoid motion planning. Various motion planning frameworks are presented in Motion Planning for Humanoid Robots, including one for skill coordination and learning, and one for manipulating and grasping tasks. The problem of planning sequences of contacts that support acyclic motion in a highly constrained environment is addressed and a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table is described. The main areas of interest include: • whole body motion planning, • task planning, • biped gait planning, and • sensor feedback for motion planning. Torque-level control of multi-contact behavior, autonomous manipulation of moving obstacles, and movement control and planning architecture are also covered. Motion Planning for Humanoid Robots will help readers to understand the current research on humanoid motion planning. It is written for industrial engineers, advanced undergraduate and postgraduate students.

Author : Manish Narsipura Sreenivasa
Publisher :
Page : 0 pages
File Size : 10,80 MB
Release : 2010
Category :
ISBN :

GET BOOK

Humanoid robotics is coming of age with faster and more agile robots. To compliment the physical complexity of humanoid robots, the robotics algorithms being developed to derive their motion have also become progressively complex. The work in this thesis spans across two research fields, human neuroscience and humanoid robotics, and brings some ideas from the former to aid the latter. By exploring the anthropological link between the structure of a human and that of a humanoid robot we aim to guide conventional robotics methods like local optimization and task-based inverse kinematics towards more realistic human-like solutions. First, we look at dynamic manipulation of human hand trajectories while playing with a yoyo. By recording human yoyo playing, we identify the control scheme used as well as a detailed dynamic model of the hand-yoyo system. Using optimization this model is then used to implement stable yoyo-playing within the kinematic and dynamic limits of the humanoid HRP-2. The thesis then extends its focus to human and humanoid locomotion. We take inspiration from human neuroscience research on the role of the head in human walking and implement a humanoid robotics analogy to this. By allowing a user to steer the head of a humanoid, we develop a control method to generate deliberative whole-body humanoid motion including stepping, purely as a consequence of the head movement. This idea of understanding locomotion as a consequence of reaching a goal is extended in the final study where we look at human motion in more detail. Here, we aim to draw to a link between “invariants” in neuroscience and “kinematic tasks” in humanoid robotics. We record and extract stereotypical characteristics of human movements during a walking and grasping task. These results are then normalized and generalized such that they can be regenerated for other anthropomorphic figures with different kinematic limits than that of humans. The final experiments show a generalized stack of tasks that can generate realistic walking and grasping motion for the humanoid HRP-2. The general contribution of this thesis is in showing that while motion planning for humanoid robots can be tackled by classical methods of robotics, the production of realistic movements necessitate the combination of these methods with the systematic and formal observation of human behavior.

Author : Nicoletta Noceti
Publisher : Springer Nature
Page : 351 pages
File Size : 20,50 MB
Release : 2020-07-09
Category : Computers
ISBN : 3030467325

GET BOOK

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 : Maziar Ahmad Sharbafi
Publisher : Butterworth-Heinemann
Page : 698 pages
File Size : 12,59 MB
Release : 2017-11-21
Category : Technology & Engineering
ISBN : 0128037741

GET BOOK

Bioinspired Legged Locomotion: Models, Concepts, Control and Applications explores the universe of legged robots, bringing in perspectives from engineering, biology, motion science, and medicine to provide a comprehensive overview of the field. With comprehensive coverage, each chapter brings outlines, and an abstract, introduction, new developments, and a summary. Beginning with bio-inspired locomotion concepts, the book's editors present a thorough review of current literature that is followed by a more detailed view of bouncing, swinging, and balancing, the three fundamental sub functions of locomotion. This part is closed with a presentation of conceptual models for locomotion. Next, the book explores bio-inspired body design, discussing the concepts of motion control, stability, efficiency, and robustness. The morphology of legged robots follows this discussion, including biped and quadruped designs. Finally, a section on high-level control and applications discusses neuromuscular models, closing the book with examples of applications and discussions of performance, efficiency, and robustness. At the end, the editors share their perspective on the future directions of each area, presenting state-of-the-art knowledge on the subject using a structured and consistent approach that will help researchers in both academia and industry formulate a better understanding of bioinspired legged robotic locomotion and quickly apply the concepts in research or products. Presents state-of-the-art control approaches with biological relevance Provides a thorough understanding of the principles of organization of biological locomotion Teaches the organization of complex systems based on low-dimensional motion concepts/control Acts as a guideline reference for future robots/assistive devices with legged architecture Includes a selective bibliography on the most relevant published articles

Author : Genci Capi
Publisher :
Page : pages
File Size : 41,18 MB
Release : 2007
Category :
ISBN : 9783902613004

GET BOOK

This paper proposed a new method for humanoid robot gait generation based on several objective functions. The proposed method is based on multiobjective evolutionary algorithm. In our work, we considered two competing objective functions: MCE and MTC. Based on simulation and experimental results, we conclude: Multiobjective evolution is efficient because optimal humanoid robot gaits with completely different characteristics can be found in one simulation run. The nondominated solutions in the obtained Pareto-optimal set are well distributed and have satisfactory diversity characteristics. The optimal gaits generated by simulation gave good performance when they were tested in the real hardware of "Bonten-Maru" humanoid robot. The optimal gait reduces the energy consumption and increases the stability during the robot motion. In the future, it will be interesting to investigate if the robot can learn in real time to switch between different gaits based on the environment conditions. In uneven terrains MTC gaits will be more.

Author : Nasser Sadati
Publisher : John Wiley & Sons
Page : 0 pages
File Size : 16,65 MB
Release : 2012-10-16
Category : Technology & Engineering
ISBN :

GET BOOK

A much-needed, state-of-the-art guide on building complex legged robots Robot control of dynamical legged locomotion has seen tremendous advances in recent decades, with hundreds of walking mechanisms being built in laboratories worldwide, helping people with disabilities and serving as replacements for humans operating in hazardous environments. This book addresses the need in the field for a comprehensive review of motion planning algorithms and hybrid control methodologies for complex legged robots. Introducing a multidisciplinary systems engineering approach for tackling many challenges posed by legged locomotion, the book provides the engineering detail readers' need to achieve dynamical legged locomotion, including hybrid models for planar and 3D legged robots, as well as hybrid control schemes for asymptotically stabilizing periodic orbits in these closed-loop systems. Researchers and practicing engineers familiar with robotics and control systems will gain a thorough understanding of: Hybrid systems and systems with impulse effects Offline and online motion planning algorithms to generate periodic walking and running motions Two-level control schemes, including within-stride feedback laws to reduce the dimension of the hybrid systems Continuous-time update laws to minimize a general cost function online Event-based update laws to asymptotically stabilize periodic orbits Complete with downloadable MATLAB code of the control algorithms and schemes used in the book, Hybrid Control and Motion Planning of Dynamical Legged Locomotion is an invaluable guide to the latest developments and future trends in dynamical legged locomotion.

Author : Jaemin Lee (Ph. D.)
Publisher :
Page : 0 pages
File Size : 18,65 MB
Release : 2022
Category :
ISBN :

GET BOOK

The fundamental objective of robotics is to enhance the productivity of humans while interacting in potentially unstructured environments. In this sense, Human-centered robots must be fast, stable, and robust when performing varied and complicated tasks during mission execution. Although industrial robots have seen some advancements regarding motion planning and control, they are largely limited to simple pre-defined tasks in structured environments. However, to achieve highly dynamic motions for dexterous manipulation or agile locomotion in complex robots, we need to consider the use of nonlinear dynamics, complex constraints, multiple contacts, disturbances, and uncertainties. These are fundamental requirements needed to advance the use of general purpose robots dynamically interacting in a wider variety of environments. Therefore, this thesis addresses challenges that arise from the employment of optimization techniques and sophisticated realtime algorithms for the control and deployment of realistic and practical robots in human environments. Considering the above challenges, we propose efficient trajectory generation and trajectory tracking methods as the next paradigms for whole-body control (WBC). First, we formulate a class of motion planning problems to directly obtain dynamically feasible state trajectories in multi-contact robots and the corresponding control inputs. Typically, it takes a tremendous amount of time to solve the end-to-end trajectory generation problem using large-scale standard Nonlinear Programming (NLP). We propose a new sampling-based method together with a Partially Observable Markov Decision Process to break down the trajectory generation problem into tractable parts. In doing so, the number of decision variables is drastically reduced. As a result, we solve the optimization problem much faster than using existing NLP techniques. In addition, we incorporate reachability analysis tools for determining whether the planned trajectories are reachable and discard unfeasible trajectories during optimization. Because simplified models are frequently utilized in locomotion studies to generate walking patterns, planned contact locations may not be feasible due to model mismatch and robot constraints. In contrast, our method enables the generation of dynamically feasible trajectories to reach planned contact location considering full-body dynamics and realistic constraints. The proposed methods are applied to contact constrained manipulation and bipedal locomotion problems to enhance capabilities of robots maneuvering in complex environments without slip or loss of balance. Second, we explore the fundamentals of WBC and use this insight to push forward the capabilities of WBC approaches. One of the problems we explore is the verification of stability of legged robots under unknown external perturbations. In such cases, the closed-loop control system controlled by WBC approaches may become unstable if external perturbations are not properly analyzed with stability verification. To verify stability, we leverage the so-called Centroidal Dynamics of legged robots and a type of WBC dubbed Whole-Body Locomotion Control (WBLC). Using a feedback-linearized state-space model, we obtain appropriate feedback gains for WBC to make our robot stable and robust under perturbations. Another challenge of WBC stems from the reliance on classical feedback control theory. Classical PD control is unsuitable for a noisy system, therefore WBC cannot be directly applied to stochastic systems. Classical WBC approaches do not consider the covariance of the terminal states as constraints which is a more efficient way to control robots with precision. We propose a new control approach, called Hierarchical Covariance Control (HCC) to enforce covariance constraints. Our proposed HCC is a stochastic version of WBC to decrease task errors when uncertainty is substantial. The last improvement I explore regarding WBC is the employment of Model Predictive Control (MPC) instead of solving an instantaneous optimization problem, which cannot guarantee global optimality. As such, we consider longer receding time horizons for MPC, thus improving the tracking performance by reducing the accumulated error norm while executing hierarchical tasks. Overall, our research focuses on the end-to-end process spanning trajectory planning to feedback control enabling the generating of multi-contact and constrained dynamic motions of complex robots operating in realistic setups. The various contributions of this thesis are in the areas of computational efficiency for whole-body trajectory generation, robustness of WBC control algorithms, and significant improvements in trajectory tracking using WBC algorithms. We verify the proposed approaches both in simulations and real experiments using various robotic systems

Author : Oscar Efrain Ramos Ponce
Publisher :
Page : 137 pages
File Size : 32,4 MB
Release : 2014
Category :
ISBN :

GET BOOK

This thesis aims at providing a solution to the problem of motion generation for humanoid robots. The proposed framework generates whole-body motion using the complete robot dynamics in the task space satisfying contact constraints. This approach is known as operational-space inverse-dynamics control. The specification of the movements is done through objectives in the task space, and the high redundancy of the system is handled with a prioritized stack of tasks where lower priority tasks are only achieved if they do not interfere with higher priority ones. To this end, a hierarchical quadratic program is used, with the advantage of being able to specify tasks as equalities or inequalities at any level of the hierarchy. Motions where the robot sits down in an armchair and climbs a ladder show the capability to handle multiple non-coplanar contacts. The generic motion generation framework is then applied to some case studies using HRP-2 and Romeo. Complex and human-like movements are achieved using human motion imitation where the acquired motion passes through a kinematic and then dynamic retargeting processes. To deal with the instantaneous nature of inverse dynamics, a walking pattern generator is used as an input for the stack of tasks which makes a local correction of the feet position based on the contact points allowing to walk on non-planar surfaces. Visual feedback is also introduced to aid in the walking process. Alternatively, for a fast balance recovery, the capture point is introduced in the framework as a task and it is controlled within a desired region of space. Also, motion generation is presented for CHIMP which is a robot that needs a particular treatment.

Author : Namrata Balakrishnan
Publisher :
Page : 100 pages
File Size : 40,53 MB
Release : 2019
Category :
ISBN :

GET BOOK

In the domain of social robotics, robots have recently been used in conversational interaction with humans. In this thesis, research was conducted to help create a system for imitation learning. In this system, a trainer trains a robot to be a teacher. The robotic teacher interacts with other humans in order to teach them the task that the robot was trained on. The method of 'Teaching by Demonstration' was used, where an ideal motion is performed by a trainer. This ideal motion is learned by the robot and replayed in the subsequent interaction with humans. If the replayed motion is copied by the human, the closeness of the motion performed by the human and the robot are compared. The task is then repeated until the desired optimal motion (as performed by the trainer) is obtained from the human subject. The main focus of the thesis is to define a general imitation system that can encode different motions which are beneficial in social robotics. A technique called Dynamic Movement Primitives (DMPs) was selected as the method for recording and generating the generalized robotic motions. The human-robot interaction gestures are compared using another algorithm called Dynamic Time Warping (DTW) and the validity of DTW as a comparison metric was also studied. DMPs are a set of non-linear differential equations which are used as the framework for describing human motion in a generalizable manner. A motion can be expressed as a combination of the learnt movement primitives. The DMPs have the flexibility to encode any motion into a set of differential equations by just adjusting certain parameters. The task/ motion that the robot is to teach a human subject is learnt using the DMPs. Once the motion is taught to the human subject, the gesture performed by the subject and the motion executed by the robot are juxtaposed and analyzed using the DTW method. DTW is an algorithm which analyzes motion series that change temporally. Similarities between the gestures performed by the robot and the imitation done by the human are studied using DTW. Trials are performed to validate the utility of DTW as an effective measure for comparison.