Conventionally, tendon-driven manipulators implement some force-based controller using either tension feedback or dynamic models of the actuator. The force control allows the system to maintain proper tensions on the tendons. In some cases, whether it is due to the lack of tension feedback or actuator torque control, a purely position-based controller is needed. This work compares three position controllers for tendon-driven manipulators that implement a nested actuator position controller. A new controller is introduced that achieves the best overall performance with regards to speed, accuracy, and transient behavior. To compensate for the lack of tension control, the controller nominally maintains the internal tension on the tendons through a range-space constraint on the actuator positions. These control laws are validated experimentally on the Robonaut-2 humanoid hand.

The second generation Robonaut hand has many advantages over its predecessor. This mechatronic device is more dexterous and has improved force control and sensing giving it the capability to grasp and actuate a wider range of tools. It can achieve higher peak forces at higher speeds than the original. Developed as part of a partnership between General Motors and NASA, the hand is designed to more closely approximate a human hand. Having a more anthropomorphic design allows the hand to attain a larger set of useful grasps for working with human interfaces. Key to the hand's improved performance is the use of lower friction drive elements and a redistribution of components from the hand to the forearm, permitting more sensing in the fingers and palm where it is most important. The following describes the design, mechanical/electrical integration, and control features of the hand. Lessons learned during the development and initial operations along with planned refinements to make it more effective are presented.

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.

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.

It is frequently accepted that tactile sensing must play a key role in robust manipulation and assembly. The potential exists to complement the gross shape information that vision or range sensors can provide with fine-scale information about the texture, stiffness, and shape of the object grasped. Nevertheless, no widely accepted tactile sensing technology currently exists for robot hands. Furthermore, while several proposals exist in the robotics literature regarding how to use tactile sensors to improve manipulation, there is little consensus. This paper describes the electro-mechanical design of the Robonaut 2 phalange load cell. This is a miniature load cell suitable for mounting on the phalanges of humanoid robot fingers. The important design characteristics of these load cells are the shape of the load cell spring element and the routing of small-gauge wires from the sensor onto a circuit board. The paper reports results from a stress analysis of the spring element and establishes the theoretical sensitivity of the device to loads in different directions. The paper also compares calibrated load cell data to ground truth load measurements for four different manufactured sensors. Finally, the paper analyzes the response of the load cells in the context of a flexible materials localization task.

Simulations of objects whose interaction change due to intermittent contact, collision, sticking or slipping traditionally employ mathematical models of the system in each topology. Usually these simulations rely on a tedious process of deriving separate dynamical equations for each topology and sophisticated switching logic to select the appropriate model. We introduce a unified method which requires deriving only a single set of equations and simple switching.

We are in the process of building a biped robot capable of highly dynamic maneuvers. Implementing such maneuvers in real-time entails heuristic controllers founded on a grasp of the dynamics. We present here an analysis of the mechanics of biped running and a control strategy for stable running. By expounding on the direction of the ground-reaction forces and utilizing an impulse representation of the contact phase, we present a tractable model of the mechanics. This model motivates the control strategy and provides a basis for extending the strategy to more general systems. The control strategy consists of a simple set of three rules, where the key rule considers the leg-length upon liftoff. This rule offers a simple control for both steady-state and accelerated running. We present the equilibrium running index as a characterization of biped running. It relates the parameters affecting running and has broad applicability to biological and robotic systems. The control strategy was verified in simulation and the results are presented.

Balance maintenance and upright posture recovery under unexpected environmental forces are key requirements for safe and successful co-existence of humanoid robots in normal human environments. In this paper we present a two-phase control strategy for robust balance maintenance under a force disturbance. The first phase, called the reflex phase, is designed to withstand the immediate effect of the force. The second phase is the recovery phase where the system is steered back to a statically stable “home” posture. The reflex control law employs angular momentum and is characterized by its counter-intuitive quality of “yielding” to the disturbance. The recovery control employs a general scheme of seeking to maximize the potential energy and is robust to local ground surface feature. Biomechanics literature indicates a similar strategy in play during human balance maintenance.

In this paper we describe a software system built to coordinate an autonomous vehicle with variable configuration ability operating in rough terrain conditions. The paper describes the system architecture, with an emphasis on the action planning function. This is intended to work with a proprioceptive algorithm that continuously coordinates wheel torques and suspension forces and positions to achieve optimal terrain crossing performance.