a) 3D shape proprioception results. (b) Sensing and data collection system.Fig. 1. Proprioception of a Baymax-shaped soft body. The predicted 3D shapes (point clouds in the bottom row) in (a) are inferred solely via RGB images from cameras embedded inside the soft body in (b) (red triangles). The point colors indicate depth increasing from blue to red. The ground truth 3D shapes (top row) in (a) are captured by an RGBD camera (Kinect) in (b). The predicted 3D shapes align well with the ground truth.Abstract-Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neural network (CNN) produces a latent code representing the deformation state, which can then be used to reconstruct the body's 3D shape using another neural network. We test the framework on various soft bodies, such as a Baymaxshaped toy, a latex balloon, and some soft robot fingers, and achieve real-time computation (≤2.5ms/frame) for robust shape estimation with high precision (≤1% relative error) and high resolution. We believe the method could be applied to soft robotics and human-robot interaction for proprioceptive shape sensing.

Platooning of heavy-duty vehicles (HDVs) is a key component of smart and connected highways and is expected to bring remarkable fuel savings and emission reduction. In this paper, we study the coordination of HDV platooning on a highway section. We model the arrival of HDVs as a Poisson process. Multiple HDVs are merged into one platoon if their headways are below a given threshold. The merging is done by accelerating the following vehicles to catch up with the leading ones. We characterize the following random variables: (i) platoon size, (ii) headway between platoons, and (iii) travel time increment due to platoon formation. We formulate and solve an optimization problem to determine the headway threshold for platooning that leads to minimal cost (time plus fuel). We also compare our results with that from Simulation of Urban MObility (SUMO).

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