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Robonaut 2 - The first humanoid robot in space
Abstract and Figures
NASA and General Motors have developed the second generation Robonaut, Robonaut 2 or R2, and it is scheduled to arrive on the International Space Station in early 2011 and undergo initial testing by mid-year. This state of the art, dexterous, anthropomorphic robotic torso has significant technical improvements over its predecessor making it a far more valuable tool for astronauts. Upgrades include: increased force sensing, greater range of motion, higher bandwidth, and improved dexterity. R2's integrated mechatronic design results in a more compact and robust distributed control system with a fraction of the wiring of the original Robonaut. Modularity is prevalent throughout the hardware and software along with innovative and layered approaches for sensing and control. The most important aspects of the Robonaut philosophy are clearly present in this latest model's ability to allow comfortable human interaction and in its design to perform significant work using the same hardware and interfaces used by people. The following describes the mechanisms, integrated electronics, control strategies, and user interface that make R2 a promising addition to the Space Station and other environments where humanoid robots can assist people.
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This paper presents a compliant variable admittance adaptive fixed-time sliding mode control (SMC) algorithm for trajectory tracking of robotic manipulators. Specifically, a compliant variable admittance algorithm and an adaptive fixed-time SMC algorithm are combined to construct a double-loop control structure. In the outer loop, the variable admittance algorithm is developed to adjust admittance parameters during a collision to minimize the collision time, which gives the robot compliance property and reduce the rigid collision influence. Then, by employing the Lyapunov theory and the fixed-time stability theory, a new nonsingular sliding mode manifold is proposed and an adaptive fixed-time SMC algorithm is presented in the inner loop. More precisely, this approach enables rapid convergence, enhanced steady-state tracking precision, and a settling time that is independent of system initial states. As a result, the effectiveness and improved performance of the proposed algorithm are demonstrated through extensive simulations and experimental results.
The advent of cloud robotics has transformed traditional robotic systems by integrating them with the vast computational power of cloud infrastructure. This chapter explores the pivotal role of communication protocols in facilitating seamless interactions between physical robots and the cloud. The foundation of cloud robotics lies in addressing specific challenges such as low-latency, reliability, scalability, and security. Various communication protocols, including MQTT, RESTful APIs, and WebSocket, are examined for their suitability in different applications, ranging from data offloading to real-time monitoring. Security considerations are paramount in the transmission of sensitive data between robots and cloud servers, necessitating encryption, authentication, and authorization mechanisms. The chapter also delves into the importance of standardization and interoperability to foster collaboration among diverse robotic systems.
This article applies high-order differential estimation to the motion planning of humanoid robots for the first time. A multiobjective optimization model and the corresponding optimal policy are designed from the perspective of solving time-varying linear equations. This method can avoid the calculation of the Jacobian matrix pseudo-inverse and its derivative, reduce energy consumption, and achieve smooth human-like robot motions. High-order differential estimation is realized by cascading multiple integration-enhanced differentiators, which estimate the first derivative based on hybrid error and quasi-sliding mode techniques. The merits of the differentiator include high accuracy in estimating high-order derivatives and the elimination of chattering. Theoretical analyses verify that the proposed differentiator and the differentiator-based solver have asymptotic convergence. Simulations prove that the integration-enhanced differentiator and the differentiator-based method have excellent performance. Experiments illustrate that the designed solver for the motion planning of a humanoid upper-body robot can track desired trajectories and perform carrying tasks.
This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure, control and decision-making, and perception and interaction, a holistic overview of the current state of humanoid robot research is presented. Furthermore, emerging challenges in the field are identified, emphasizing the necessity for a deeper understanding of biological motion mechanisms, improved structural design, enhanced material applications, advanced drive and control methods, and efficient energy utilization. The integration of bionics, brain-inspired intelligence, mechanics, and control is underscored as a promising direction for the development of advanced humanoid robotic systems. This paper serves as an invaluable resource, offering insightful guidance to researchers in the field, while contributing to the ongoing evolution and potential of humanoid robots across diverse domains.
Existing tendon-driven fingers have applied force control through independent tension controllers on each tendon, i.e. in the tendon-space. The coupled kinematics of the tendons, however, cause such controllers to exhibit a transient coupling in their response. This problem can be resolved by alternatively framing the controllers in the joint-space of the manipulator. This work presents a joint-space torque control law that demonstrates both a decoupled and significantly faster response than an equivalent tendon-space formulation. The law also demonstrates greater speed and robustness than comparable PI controllers. In addition, a tension distribution algorithm is presented here to allocate forces from the joints to the tendons. It allocates the tensions so that they satisfy both an upper and lower bound, and it does so without requiring linear programming or open-ended iterations. The control law and tension distribution algorithm are implemented on the robotic hand of Robonaut-2.
Humanoid robots are intended to interact with unstructured environments and to perform diverse applications. Often, such work involves manipulating an object cooperatively with multiple hands or fingers. This work presents an impedance based control framework for such cases with multi-priority tasking. The primary task governs the impedance response of the object and a secondary task governs the impedance response of the joints. Using a novel transformation, the primary task may specify a subset of the object degrees of freedom (DOFs), allocating the remaining DOFs to the secondary task. This results in an integrated null space that includes not only the redundant DOFs of each manipulator independantly, but also the free DOFs of the object shared across the manipulators.
The present paper describes an implementation of fast running motions involving a humanoid robot. Two important technologies are described: a motion generation and a balance control. The motion generation is a unified way to design both walking and running and can generate the trajectory with the vertical conditions of the Center Of Mass (COM) in short calculation time. The balance control enables a robot to maintain balance by changing the positions of the contact foot dynamically when the robot is disturbed. This control consists of 1) compliance control without force sensors, in which the joints are made compliant by feed-forward torques and adjustment of gains of position control, and 2) feedback control, which uses the measured orientation of the robot's torso used in the motion generation as an initial condition to decide the foot positions. Finally, a human-sized humanoid robot that can run forward at 7.0 [km/h] is presented.
Impedance control is well-suited to robot manipulation applications because it gives the designer a measure of control over how the manipulator to conforms to the environment. However, in the context of end-effector impedance control when the robot manipulator is redundant with respect to end-effector configuration, the question arises regarding how to control the impedance of the redundant joints. This paper considers multi-priority impedance control where a second-priority joint space impedance operates in the null space of a first-priority Cartesian impedance at the end-effector. A control law is proposed that realizes both impedances while observing the priority constraint such that a weighted quadratic error function is optimized. This control law is shown to be a generalization of several motion and impedance control laws found in the literature. The paper makes explicit two forms of the control law. In the first, parametrization by passive inertia values allows the control law to be implemented without requiring end-effector force measurements. In the second, a class of parametrizations is introduced that makes the null space impedance independent of end-effector forces. The theoretical results are illustrated in simulation.
The abridged contents include: Kinematic and force analysis of articulated hands: contact - freedom and constraint; contacts in groups; force application and velocity analysis; force error analysis. Manipulator grasping and pushing operations: theory of pushing; application; conclusion. Index. This book, based on the doctoral dissertations of the two authors, examines several aspects of manipulating objects. At present, the authors believe that industrial robots are not used effectively. Tasks performed by robot manipulators are now limited to simple packing and stacking operations. By understanding the principles discussed in this book, better industrial robots are presented.
The implementation of impedance control is considered. A feedback control algorithm for imposing a desired cartesian impedance on the end-point of a nonlinear manipulator is presented. This algorithm completely eliminates the need to solve the 'inverse kinematics problem' in robot motion control. The modulation of end-point impedance without using feedback control is also considered, and it is shown that apparently 'redundant' actuators and degrees of freedom such as exist in the primate musculoskeletal system may be used to modulate end-point impedance and may play an essential functional role in the control of dynamic interaction.
Manipulation fundamentally requires a manipulator to be mechanically coupled to the object being manipulated. A consideration of the physical constraints imposed by dynamic interaction shows that control of a vector quantity such as position or force is inadequate and that control of the manipulator impedance is also necessary. Techniques for control of manipulator behaviour are presented which result in a unified approach to kinematically constrained motion, dynamic interaction, target acquisition and obstacle avoidance.
Yoyo playing may seem easy for a human, but it is a challenging problem for a humanoid robot. This paper presents an approach to generate yoyo motions for the humanoid robot, HRP-2, based on motion recorded from human yoyo playing, dynamical models and numerical optimal control techniques. We recorded vertical yoyo playing of 4 subjects measuring yoyo height and rotation angle as well as the corresponding hand motions. A detailed multi-phase yoyo model with impact collisions and control patterns of human yoyo playing were identified from these measurements. Based on this knowledge, reliable yoyo motions within the feasibility ranges of HRP-2 were generated using optimal control. The resulting motions have been implemented on the robot using open-loop and event-based quasi open-loop control strategies.
The Robotics Technology Branch at the NASA Johnson Space Center is developing robotic systems to assist astronauts in space. One such system, Robonaut, is a humanoid robot with the dexterity approaching that of a suited astronaut. Robonaut currently has two dexterous arms and hands, a three degree-of-freedom articulating waist, and a two degree-of-freedom neck used as a camera and sensor platform. In contrast to other space manipulator systems, Robonaut is designed to work within existing corridors and use the same tools as space walking astronauts. Robonaut is envisioned as working with astronauts, both autonomously and by teleoperation, performing a variety of tasks including, routine maintenance, setting up and breaking down worksites, assisting crew members while outside of spacecraft, and serving in a rapid response capacity.