Impedence Control for Variable Stiffness Mechanisms with Nonlinear Joint Coupling

The current discussion on physical human robot interaction and the related safety aspects, but also the interest of neuro-scientists to validate their hypotheses on human motor skills with bio-mimetic robots, led to a recent revival of tendondriven robots. In this paper, the modeling of tendon-driven elastic systems with nonlinear couplings is recapitulated. A control law is developed that takes the desired joint position and stiffness as input. Therefore, desired motor positions are determined that are commanded to an impedance controller. We give a physical interpretation of the controller. More importantly, a static decoupling of the joint motion and the stiffness variation is given. The combination of active (controller) and passive (mechanical) stiffness is investigated. The controller stiffness is designed according to the desired overall stiffness. A damping design of the impedance controller is included in these considerations. The controller performance is evaluated in simulation

Co-exploring Actuator Antagonism and Bio-inspired Control in a Printable Robot Arm

The human arm is capable of performing fast targeted movements with high precision, say in pointing with a mouse cursor, but is inherently ‘soft’ due to the muscles, tendons and other tissues of which it is composed. Robot arms are also becoming softer, to enable robustness when operating in real-world environments, and to make them safer to use around people. But softness comes at a price, typically an increase in the complexity of the control required for a given task speed/accuracy requirement. Here we explore how fast and precise joint movements can be simply and effectively performed in a soft robot arm, by taking inspiration from the human arm. First, viscoelastic actuator-tendon systems in an agonist-antagonist setup provide joints with inherent damping, and stiffness that can be varied in real-time through co-contraction. Second, a light-weight and learnable inverse model for each joint enables a fast ballistic phase that drives the arm close to a desired equilibrium point and co-contraction tuple, while the final adjustment is done by a feedback controller. The approach is embodied in the GummiArm, a robot which can almost entirely be printed on hobby-grade 3D printers. This enables rapid and iterative co-exploration of ‘brain’ and ‘body’, and provides a great platform for developing adaptive and bio-inspired behaviours

Design of an Anthropomorphic Robotic Hand for Space Operations

Robotic end-effectors provide the link between machines and the environment. The evolution of end-effector design has traded off between simplistic single-taskers and highly complex multi-function grippers. For future space operations, launch payload weight and the wide range of desired tasks necessitate a highly dexterous design with strength and manipulation capabilities matching those of the suited astronaut using EVA tools. The human hand provides the ideal parallel for a dexterous end-effector design. This thesis discusses efforts to design an anthropomorphic robotic hand, focusing on the detailed design, fabrication, and testing of an individual modular finger with considerations into overall hand configuration. The research first aims to define requirements for anthropomorphism and compare the geometry and motion of the design to that of the human hand. Active and passive ranges of motion are studied along with coupled joint behavior and grasp types. The second objective is to study the benefits and drawbacks of an active versus passive actuation systems. Tradeoffs between controllability and packaging of actuator assemblies are considered. Finally, a kinematic model is developed to predict tendon tensions and tip forces in different configurations. The esults show that the measured forces are consistent with the predictive model. In addition, the coupled joint motion shows similar behavior to that of the human hand

Variable stiffness robotic hand for stable grasp and flexible handling

Robotic grasping is a challenging area in the field of robotics. When interacting with an object, the dynamic properties of the object will play an important role where a gripper (as a system), which has been shown to be stable as per appropriate stability criteria, can become unstable when coupled to an object. However, including a sufficiently compliant element within the actuation system of the robotic hand can increase the stability of the grasp in the presence of uncertainties. This paper deals with an innovative robotic variable stiffness hand design, VSH1, for industrial applications. The main objective of this work is to realise an affordable, as well as durable, adaptable, and compliant gripper for industrial environments with a larger interval of stiffness variability than similar existing systems. The driving system for the proposed hand consists of two servo motors and one linear spring arranged in a relatively simple fashion. Having just a single spring in the actuation system helps us to achieve a very small hysteresis band and represents a means by which to rapidly control the stiffness. We prove, both mathematically and experimentally, that the proposed model is characterised by a broad range of stiffness. To control the grasp, a first-order sliding mode controller (SMC) is designed and presented. The experimental results provided will show how, despite the relatively simple implementation of our first prototype, the hand performs extremely well in terms of both stiffness variability and force controllability

Adaptive Optimal Feedback Control with Learned Internal Dynamics Models

Optimal Feedback Control (OFC) has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. Recent developments, such as the Iterative Linear Quadratic Gaussian (ILQG) algorithm, have focused on the case of non-linear, but still analytically available, dynamics. For realistic control systems, however, the dynamics may often be unknown, difficult to estimate, or subject to frequent systematic changes. In this chapter, we combine the ILQG framework with learning the forward dynamics for simulated arms, which exhibit large redundancies, both, in kinematics and in the actuation. We demonstrate how our approach can compensate for complex dynamic perturbations in an online fashion. The specific adaptive framework introduced lends itself to a computationally more efficient implementation of the ILQG optimisation without sacrificing control accuracy – allowing the method to scale to large DoF systems

Robotics of human movements

The construction of robotic systems that can move the way humans do, with respect to agility, stability and precision, is a necessary prerequisite for the successful integration of robotic systems in human environments. We explain human-centered views on robotics, based on the three basic ingredients (1) actuation; (2) sensing; and (3) control, and formulate detailed examples thereof