A variable stiffness soft gripper using granular jamming and biologically inspired pneumatic muscles

As the domains in which robots operate change the objects a robot may be required to grasp and manipulate are likely to vary significantly and often. Furthermore there is increasing likelihood that in the future robots will work collaboratively alongside people. There has therefore been interest in the development of biologically inspired robot designs which take inspiration from nature. This paper presents the design and testing of a variable stiffness, three fingered soft gripper which uses pneumatic muscles to actuate the fingers and granular jamming to vary their stiffness. This gripper is able to adjust its stiffness depending upon how fragile/deformable the object being grasped is. It is also lightweight and low inertia making it better suited to operation near people. Each finger is formed from a cylindrical rubber bladder filled with a granular material. It is shown how decreasing the pressure inside the finger increases the jamming effect and raises finger stiffness. The paper shows experimentally how the finger stiffness can be increased from 21 to 71 N/m. The paper also describes the kinematics of the fingers and demonstrates how they can be position-controlled at a range of different stiffness values

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

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In this paper we briefly address DLR’s (German Aerospace Center) background in space robotics by hand of corresponding milestone projects including systems on the International Space Station. We then discuss the key technologies needed for the development of an artificial “robonaut ” generation with mechatronic ultra-lightweight arms and multifingered hands. The third arm generation is nearly finished now, approaching the limits of what is technologically achievable today with respect to light weight and power losses. In a similar way DLR’s second generation of artificial four-fingered hands was a big step towards higher reliability, manipulability and overall performance. KEY WORDS—space-robotics, lightweight robotics, joint/ torque control, mechatronic