Directly 3D Printed, Pneumatically Actuated Multi-Material Robotic Hand

Soft robotic manipulators with many degrees of freedom can carry out complex tasks safely around humans. However, manufacturing of soft robotic hands with several degrees of freedom requires a complex multi-step manual process, which significantly increases their cost. We present a design of a multi-material 15 DoF robotic hand with five fingers including an opposable thumb. Our design has 15 pneumatic actuators based on a series of hollow chambers that are driven by an external pressure system. The thumb utilizes rigid joints and the palm features internal rigid structure and soft skin. The design can be directly 3D printed using a multi-material additive manufacturing process without any assembly process and therefore our hand can be manufactured for less than 300 dollars. We test the hand in conjunction with a low-cost vision-based teleoperation system on different tasks.Comment: 7 pages, 16 figure

Observer-based Control of Inflatable Robot with Variable Stiffness

In the last decade, soft robots have been at the forefront of a robotic revolution. Due to the flexibility of the soft materials employed, soft robots are equipped with a capability to execute new tasks in new application areas -beyond what can be achieved using classical rigid-link robots. Despite these promising properties, many soft robots nowadays lack the capability to exert sufficient force to perform various real-life tasks. This has led to the development of stiffness-controllable inflatable robots instilled with the ability to modify their stiffness during motion. This new capability, however, poses an even greater challenge for robot control. In this paper, we propose a model-based kinematic control strategy to guide the tip of an inflatable robot arm in its environment. The bending of the robot is modelled using an Euler-Bernoulli beam theory which takes into account the variation of the robot's structural stiffness. The parameters of the model are estimated online using an observer based on the Extended Kalman Filter (EKF). The parameters' estimates are used to approximate the Jacobian matrix online and used to control the robot's tip considering also variations in the robot's stiffness. Simulation results and experiments using a fabric-based planar 3-degree-of-freedom (DOF) inflatable manipulators demonstrate the promising performance of the proposed control algorithm

Safe Grasping with a Force Controlled Soft Robotic Hand

Safe yet stable grasping requires a robotic hand to apply sufficient force on the object to immobilize it while keeping it from getting damaged. Soft robotic hands have been proposed for safe grasping due to their passive compliance, but even such a hand can crush objects if the applied force is too high. Thus for safe grasping, regulating the grasping force is of uttermost importance even with soft hands. In this work, we present a force controlled soft hand and use it to achieve safe grasping. To this end, resistive force and bend sensors are integrated in a soft hand, and a data-driven calibration method is proposed to estimate contact interaction forces. Given the force readings, the pneumatic pressures are regulated using a proportional-integral controller to achieve desired force. The controller is experimentally evaluated and benchmarked by grasping easily deformable objects such as plastic and paper cups without neither dropping nor deforming them. Together, the results demonstrate that our force controlled soft hand can grasp deformable objects in a safe yet stable manner.Comment: Accepted to 2020 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2020

Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing

Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and tactile sensing due to their high flexibility and elasticity. In this work, we describe the development of a vision-based proprioceptive and tactile sensor for soft robots called GelFlex, which is inspired by previous GelSight sensing techniques. More specifically, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution proprioceptive sensing and rich tactile sensing. To do so, we design features along the axial direction of the finger, which enable high-resolution proprioceptive sensing, and incorporate a reflective ink coating on the surface of the finger to enable rich tactile sensing. We design a highly underactuated exoskeleton with a tendon-driven mechanism to actuate the finger. Finally, we assemble 2 of the fingers together to form a robotic gripper and successfully perform a bar stock classification task, which requires both shape and tactile information. We train neural networks for proprioception and shape (box versus cylinder) classification using data from the embedded sensors. The proprioception CNN had over 99\% accuracy on our testing set (all six joint angles were within 1 degree of error) and had an average accumulative distance error of 0.77 mm during live testing, which is better than human finger proprioception. These proposed techniques offer soft robots the high-level ability to simultaneously perceive their proprioceptive state and peripheral environment, providing potential solutions for soft robots to solve everyday manipulation tasks. We believe the methods developed in this work can be widely applied to different designs and applications.Comment: Accepted to ICRA202

Elastic Structure Preserving Impedance Control for Nonlinearly Coupled Tendon-Driven Systems

Traditionally, most of the nonlinear control techniques for elastic robotic systems focused on achieving a desired closed-loop behavior by modifying heavily the intrinsic properties of the plant. This is also the case of elastic tendon-driven systems, where the highly nonlinear couplings lead to several control challenges. Following the current philosophy of exploiting the mechanical compliance rather than fighting it, this letter proposes an Elastic Structure Preserving impedance (ESPi) control for systems with coupled elastic tendinous transmissions. Our strategy achieves a globally asymptotically stable closed-loop system that minimally shapes the intrinsic inertial and elastic structure. %to add desired stiffness and damping on the link side. It further allows to impose a desired link-side impedance behavior. Simulations performed on the tendon-driven index finger of the DLR robot David show satisfactory results of link-side interaction behavior and set-point regulation

A Vacuum-driven Origami “Magic-ball” Soft Gripper

Soft robotics has yielded numerous examples of soft grippers that utilize compliance to achieve impressive grasping performances with great simplicity, adaptability, and robustness. Designing soft grippers with substantial grasping strength while remaining compliant and gentle is one of the most important challenges in this field. In this paper, we present a light-weight, vacuum-driven soft robotic gripper made of an origami “magic-ball” and a flexible thin membrane. We also describe the design and fabrication method to rapidly manufacture the gripper with different combinations of lowcost materials for diverse applications. Grasping experiments demonstrate that our gripper can lift a large variety of objects, including delicate foods, heavy bottles, and other miscellaneous items. The grasp force on 3D-printed objects is also characterized through mechanical load tests. The results reveal that our soft gripper can produce significant grasp force on various shapes using negative pneumatic pressure (vacuum). This new gripper holds the potential for many practical applications that require safe, strong, and simple graspingUnited States. Defense Advanced Research Projects Agency (award number FA8650-15-C-7548)National Science Foundation (U.S.) (award number 1830901)Wyss Institute for Biologically Inspired EngineeringJD.co

Dynamically Closed-Loop Controlled Soft Robotic Arm using a Reduced Order Finite Element Model with State Observer

International audienceThis paper presents a computationally efficient method to model and simulate soft robots. Finite element methods enable us to simulate and control soft robots, but require us to work with a large dimensional system. This limits their use in real-time simulation and makes those methods less suitable for control design tools. Using model order reduction, it is possible to create a reduced order system for building controllers and observers. Model reduction errors are taken into account in the design of the low-order feedback, and it is then applied to the large dimensional, unreduced model. The control architecture is based on a linearized model of the robot and enables the control of the robot around this equilibrium point. To show the performance of this control method, pose-to-pose and trajectory tracking experiments are conducted on a pneumatically actuated soft arm. The soft arm has 12 independent interior cavities that can be pressurized and cause the arm to move in three dimensions. The arm is made of a rubber material and is casted through a lost-wax fabrication technique