Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Soft robots can safely interact with environments because of their mechanical compliance. Self-collision is also employed in the modern design of soft robots to enhance their performance during different tasks. However, developing an efficient and reliable simulator that can handle the collision response well, is still a challenging task in the research of soft robotics. This paper presents a collision-aware simulator based on geometric optimization, in which we develop a highly efficient and realistic collision checking / response model incorporating a hyperelastic material property. Both actuated deformation and collision response for soft robots are formulated as geometry-based objectives. The collision-free body of a soft robot can be obtained by minimizing the geometry-based objective function. Unlike the FEA-based physical simulation, the proposed pipeline performs a much lower computational cost. Moreover, adaptive remeshing is applied to achieve the improvement of the convergence when dealing with soft robots that have large volume variations. Experimental tests are conducted on different soft robots to verify the performance of our approach

Spring-IMU Fusion Based Proprioception for Feedback Control of Soft Manipulators

This paper presents a novel framework to realize proprioception and closed-loop control for soft manipulators. Deformations with large elongation and large bending can be precisely predicted using geometry-based sensor signals obtained from the inductive springs and the inertial measurement units (IMUs) with the help of machine learning techniques. Multiple geometric signals are fused into robust pose estimations, and a data-efficient training process is achieved after applying the strategy of sim-to-real transfer. As a result, we can achieve proprioception that is robust to the variation of external loading and has an average error of 0.7% across the workspace on a pneumatic-driven soft manipulator. The realized proprioception on soft manipulator is then contributed to building a sensor-space based algorithm for closed-loop control. A gradient descent solver is developed to drive the end-effector to achieve the required poses by iteratively computing a sequence of reference sensor signals. A conventional controller is employed in the inner loop of our algorithm to update actuators (i.e., the pressures in chambers) for approaching a reference signal in the sensor-space. The systematic function of closed-loop control has been demonstrated in tasks like path following and pick-and-place under different external loads

OpenPneu: Compact platform for pneumatic actuation with multi-channels

This paper presents a compact system, OpenPneu, to support the pneumatic actuation for multi-chambers on soft robots. Micro-pumps are employed in the system to generate airflow and therefore no extra input as compressed air is needed. Our system conducts modular design to provide good scalability, which has been demonstrated on a prototype with ten air channels. Each air channel of OpenPneu is equipped with both the inflation and the deflation functions to provide a full range pressure supply from positive to negative with a maximal flow rate at 1.7 L/min. High precision closed-loop control of pressures has been built into our system to achieve stable and efficient dynamic performance in actuation. An open-source control interface and API in Python are provided. We also demonstrate the functionality of OpenPneu on three soft robotic systems with up to 10 chambers

Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This paper presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly non-linear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware. The effectiveness of our approach has been verified on pneumatic-driven soft robots for path following and interactive positioning

Efficient energy transfer in layered hybrid organic/inorganic nanocomposites: A dual function of semiconductor nanocrystals

The efficiency of energy transfer in hybrid organic/inorganic nanocomposites based on conjugated polymers and semiconductor nanocrystals is strongly dependent on both the energy transfer rate and the rate of the nonradiative recombination of the polymer. We demonstrate that the polymer nonradiative recombination can be reduced by the suppression of exciton diffusion via proper morphology engineering of a hybrid structure. In the layer-by-layer assembled nanocomposite of a conjugated polymer and CdTe nanocrystals the latter have a dual role: first, they are efficient exciton acceptors and, second, they reduce nonradiative recombination in the polymer by suppressing exciton diffusion across the layers.Fil: Lutich, Andrey A.. Ludwig Maximilians Universitat; AlemaniaFil: Pöschl, Andreas. Ludwig Maximilians Universitat; AlemaniaFil: Jiang, Guoxin. Ludwig Maximilians Universitat; AlemaniaFil: Stefani, Fernando Daniel. Ludwig Maximilians Universitat; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Susha, Andrei S.. City University of Hong Kong; ChinaFil: Rogach, Andrey L.. City University of Hong Kong; ChinaFil: Feldmann, Jochen. Ludwig Maximilians Universitat; Alemani