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The field of legged robotics has made significant advancements and shown potential practicality in various applications. Although these robots are becoming more popular, they are still not widely used due to the inherent danger when malfunctioning as well as their high cost. BALLU, Buoyancy Assisted Lightweight Legged Unit, is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to its lightweight body. This platform solves many issues that hinder current robots from operating close to humans while also providing affordability. However, the advantages gained also lead to the platform's distinct difficulties caused by severe underactuation and nonlinearities due to external forces such as buoyancy and drag. This dissertation presents a motion planning approach using data-driven techniques motivated by these challenges and its application to BALLU. The paper describes the concept of the platform, the hardware design of different generations of BALLUs, the software architecture, the nonconventional characteristics of BALLU as a legged robot, and an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process is proposed to form low-dimensional state vectors from the simulation data, and a deep neural network-based controller is trained. The controller is tested on both simulation and real-world hardware. Its performance is assessed by observing the robot's limit cycles and trajectories in Cartesian coordinates. The controller generates periodic walking sequences in simulation as well as on the real-world robot, even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLU's walking.
Walking machines have advantages over traditional vehicles, and have already accomplished tasks that wheeled or tracked robots cannot handle. Nevertheless, their use in industry and services is currently limited in scope. This book brings together methods and techniques that have been developed to deal with obstacles to wider acceptance of legged robots. Part I provides an historical overview. Part II concentrates on control techniques, as applied to Four-legged robots.
In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible ...
The development of legged robots capable of navigating in and interacting with the world is quickly advancing as new methods and techniques for sensing, decision-making, and controls expand the capabilities of state-of-the-art systems. Model-based methods, empowered by greater computing capacity and clever formulations, are imbuing systems with further physics-based understanding. While machine learning techniques, enabled by parallelized data generation and more efficient training, are imparting greater robustness to noise and abilities to handle poorly defined world features. Together these tools constitute the two major paradigms of legged robot research and while both have their shortcomings, they have complementary limitations that can be reinforced by the other's strengths. We propose MIMOC: Motion Imitation from Model-Based Optimal Control. MIMOC is a Reinforcement Learning (RL) locomotion controller that learns agile locomotion by imitating reference trajectories from model-based optimal control. MIMOC mitigates challenges faced by other motion imitation-based RL approaches because the generated reference trajectories are dynamically consistent, require no motion retargeting, and include torque references that are essential to learn dynamic locomotion. As a result, MIMOC does not require any fine-tuning to transfer the policy to the real robots. MIMOC also overcomes key issues with model-based optimal controllers. Since it is trained with simulated sensor noise and domain randomization, MIMOC is less sensitive to modeling and state estimation inaccuracies. We validate MIMOC on the Mini-Cheetah in outdoor environments over a wide variety of challenging terrain and on the MIT Humanoid in simulation. We show that MIMOC can transfer to the real-world and to different legged platforms. We also show cases where MIMOC outperforms model-based optimal controllers, and demonstrate the value of imitating torque references.
The first chapter of this book traces the history of the development of walking machines from the original ideas of man-amplifiers and military rough-ground transport to today's diverse academic and industrial research and development projects. It concludes with a brief account of research on other unusual methods of locomotion. The heart of the book is the next three chapters on the theory and engineering of legged robots. Chapter 2 presents the basics of land loco motion, going on to consider the energetics of legged movement and the description and classification of gaits. Chapter 3, dealing with the mechanics of legged vehicles, goes into leg number and arrangement, and discusses mechanical design and actuation methods. Chapter 4 deals with analysis and control, describing the aims of control theory and the methods of modelling and control which have been used for both highly dynamic robots and multi-legged machines. Having dealt with the theory of control it is necessary to discuss the computing system on which control is to be implemented. This is done in Chapter 5, which covers architectures, sensing, algorithms and pro gramming languages. Chapter 6 brings together the threads of the theory and engineering discussed in earlier chapters and summarizes the current walking machine research projects. Finally, the applications, both actual and potential, of legged locomotion are described. Introduction Research into legged machines is expanding rapidly. There are several reasons why this is happening at this particular time.
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 engineering detail 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. Complete with downloadable MATLAB code of the control algorithms and schemes used in the book, this book is an invaluable guide to the latest developments and future trends in dynamical legged locomotion.
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
Bipedal locomotion is among the most difficult challenges in control engineering. Most books treat the subject from a quasi-static perspective, overlooking the hybrid nature of bipedal mechanics. Feedback Control of Dynamic Bipedal Robot Locomotion is the first book to present a comprehensive and mathematically sound treatment of feedback design for achieving stable, agile, and efficient locomotion in bipedal robots. In this unique and groundbreaking treatise, expert authors lead you systematically through every step of the process, including: Mathematical modeling of walking and running gaits in planar robots Analysis of periodic orbits in hybrid systems Design and analysis of feedback systems for achieving stable periodic motions Algorithms for synthesizing feedback controllers Detailed simulation examples Experimental implementations on two bipedal test beds The elegance of the authors' approach is evident in the marriage of control theory and mechanics, uniting control-based presentation and mathematical custom with a mechanics-based approach to the problem and computational rendering. Concrete examples and numerous illustrations complement and clarify the mathematical discussion. A supporting Web site offers links to videos of several experiments along with MATLAB® code for several of the models. This one-of-a-kind book builds a solid understanding of the theoretical and practical aspects of truly dynamic locomotion in planar bipedal robots.
Legged robots have the potential to be highly dynamic machines capable of outperforming humans and animals in executing locomotion tasks within dangerous and unstructured environments. Unfortunately, current control methods still lack the ability to move with the agility and robustness needed to traverse arbitrary terrains with the same grace and reliability as animals. This dissertation presents the successful implementation of a novel nonlinear optimization-based Regularized Predictive Control (RPC) framework that optimizes robot states, footstep locations, and ground reaction forces over a future prediction horizon. RPC exploits expertly designed and data-driven extracted heuristics by directly embedding them in the optimization through regularization in the cost function. Well-designed regularization should bias results towards a "good enough" heuristic solution by shaping the cost space favorably, while allowing the optimization to find a better result if it exists. However, designing meaningful regularized cost functions and adequate heuristics is challenging and not straightforward. A novel framework is presented for automatically extracting and designing new principled legged locomotion heuristics by fitting simple intuitive models to simulated and experimental data using RPC. Statistically correlated relationships between desired commands, robot states, and optimal control inputs are found by allowing the optimization to more exhaustively search the cost space during offline explorations when not subjected to real-time computation constraints. This method extracts simple, but powerful heuristics that can approximate complex dynamics and account for errors stemming from model simplifications or parameter uncertainty without the loss of physical intuition. Nonlinear optimization-based controllers have shown improved capabilities in simulation, but fall short when implemented on hardware systems that must adhere to real-time computation constraints and physical limits. Various methods and algorithms critical to the success of the robot were developed to overcome these challenges. The controller is verified experimentally using the MIT Cheetah 3 and Mini Cheetah robot platforms. Results demonstrate the ability of the robot to track dynamic velocity and turn rate commands with a variety of parametrized gaits, remain upright through large impulsive and sustained disturbances, and traverse highly irregular terrains. All of these behaviors are achieved with no modifications to the controller structure and with one set of gains signifying the generalized robustness of RPC. This work represents a step towards more robust dynamic locomotion capabilities for legged robots.
The model-based investigation of motions of anthropomorphic systems is an important interdisciplinary research topic involving specialists from many fields such as Robotics, Biomechanics, Physiology, Orthopedics, Psychology, Neurosciences, Sports, Computer Graphics and Applied Mathematics. This book presents a study of basic locomotion forms such as walking and running is of particular interest due to the high demand on dynamic coordination, actuator efficiency and balance control. Mathematical models and numerical simulation and optimization techniques are explained, in combination with experimental data, which can help to better understand the basic underlying mechanisms of these motions and to improve them. Example topics treated in this book are Modeling techniques for anthropomorphic bipedal walking systems Optimized walking motions for different objective functions Identification of objective functions from measurements Simulation and optimization approaches for humanoid robots Biologically inspired control algorithms for bipedal walking Generation and deformation of natural walking in computer graphics Imitation of human motions on humanoids Emotional body language during walking Simulation of biologically inspired actuators for bipedal walking machines Modeling and simulation techniques for the development of prostheses Functional electrical stimulation of walking.