Control Design for Soft Robots based on Reduced Order Model

International audienceInspired by nature, soft robots promise disruptive advances in robotics. Soft robots are naturally compliant and exhibit nonlinear behavior, which makes their study challenging. No unified framework exists to control these robots, especially when considering their dynamics. This work proposes a methodology to study this type of robots around a stable equilibrium point. It can make the robot converge faster and with reduced oscillations to a desired equilibrium state. Using computational mechanics, a large-scale dynamic model of the robot is obtained and model reduction algorithms enable the design of low order controller and observer. A real robot is used to demonstrate the interest of the results

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

A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping

Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate

Interoceptive robustness through environment-mediated morphological development

Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies