ViSE: Vision-Based 3D Online Shape Estimation of Continuously Deformable Robots

The precise control of soft and continuum robots requires knowledge of their shape. The shape of these robots has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built-in sensors resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on placing reflective markers on all tracked components and triangulating their position using multiple motion-tracking cameras. Tracking systems are expensive and infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, we present a regression approach for 3D shape estimation using a convolutional neural network. The proposed approach takes advantage of data-driven supervised learning and is capable of real-time marker-less shape estimation during inference. Two images of a robotic system are taken simultaneously at 25 Hz from two different perspectives, and are fed to the network, which returns for each pair the parameterized shape. The proposed approach outperforms marker-less state-of-the-art methods by a maximum of 4.4% in estimation accuracy while at the same time being more robust and requiring no prior knowledge of the shape. The approach can be easily implemented due to only requiring two color cameras without depth and not needing an explicit calibration of the extrinsic parameters. Evaluations on two types of soft robotic arms and a soft robotic fish demonstrate our method's accuracy and versatility on highly deformable systems in real-time. The robust performance of the approach against different scene modifications (camera alignment and brightness) suggests its generalizability to a wider range of experimental setups, which will benefit downstream tasks such as robotic grasping and manipulation

The onset and dynamics of avalanches in a rotating cylinder: From experimental data to a new geometric model

Particle image velocimetry has been applied to measure particle velocities on the free surface of a bed of particles within a rotating cylinder during avalanching. The particle velocities were used to examine the validity of existing avalanche models and to propose an alternative model. The movement of particles depends on their location on the surface of the bed: particles located near the center of the bed travel the farthest, while the distance travelled decreases at an increasing rate for particles located farther from the center. The start of an avalanche can be determined to a single initiation point, that can also be located on the bottom half of the bed; the avalanche quickly propagates through the entire free surface, with 90% of the surface in motion within 257 ms. The experimental insight is used to formulate a new geometric model, in which three equal sized sections flow down the bed during an avalanche. The predictions of the model are confirmed by experimental mixing measurements

Onset and dynamics of avalanches in a rotating cylinder: From experimental data to a geometric model

Particle image velocimetry has been applied to measure particle velocities on the free surface of a bed of particles within a rotating cylinder during avalanching. The particle velocities were used to examine the validity of existing avalanche models and to propose an alternative model. The movement of particles depends on their location on the surface of the bed: Particles located near the center of the bed travel the farthest, while the distance traveled decreases at an increasing rate for particles located farther from the center. The start of an avalanche can be determined to a single initiation point that can be located on the bottom half of the bed; the avalanche quickly propagates through the entire free surface with 90% of the surface in motion within 257 ms (approximately 20% of the total duration of an avalanche). The experimental insight is used to formulate a geometric model, in which three equal-sized sections flow down the bed surface during an avalanch. The predictions of the model are validated by experimental mixing measurements.ISSN:1539-3755ISSN:1063-651XISSN:1095-3787ISSN:1550-237

Sinking dynamics and splitting of a granular droplet

Recent experimental results have shown that binary granular materials fluidized by combined vibration and gas flow exhibit Rayleigh-Taylor-like instabilities that manifest themselves in rising plumes, rising bubbles, and the sinking and splitting of granular droplets. This work explores the physics behind the splitting of a granular droplet that is composed of smaller and denser particles in a bed of larger and lighter particles. During its sinking motion, a granular droplet undergoes a series of binary splits resembling the fragmentation of a liquid droplet falling in a miscible fluid. However, different physical mechanisms cause a granular droplet to split. By applying particle image velocimetry and numerical simulations, we demonstrate that the droplet of high-density particles causes the formation of an immobilized zone underneath the droplet. This zone obstructs the downwards motion of the droplet and causes the droplet to spread and ultimately to split. The resulting fragments sink at inclined trajectories around the immobilized zone until another splitting event is initiated. The occurrence of consecutive splitting events is explained by the reformation of an immobilized zone underneath the droplet fragments. Our investigations identified three requirements for a granular droplet to split: (1) frictional interparticle contacts, (2) a higher density of the particles composing the granular droplet compared to the bulk particles, and (3) a minimal granular droplet diameter.ISSN:2469-990