Propagation of electromagnetically generated wake fields in inhomogeneous magnetized plasmas

Generation of wake fields by a short electromagnetic pulse in a plasma with an inhomogeneous background magnetic field and density profile is considered, and a wave equation is derived. Transmission and reflection coefficients are calculated in a medium with sharp discontinuities. Particular attention is focused on examples where the longitudinal part of the electromagnetic field is amplified for the transmitted wave. Furthermore, it is noted that the wake field can propagate out of the plasma and thereby provide information about the electron density profile. A method for reconstructing the background density profile from a measured wake field spectrum is proposed and a numerical example is given.Comment: 12 pages in LaTeX style, 11 eps figure

Multi-log grasping using reinforcement learning and virtual visual servoing

We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves problems of dynamics and path planning, where the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilize a virtual camera to provide an image stream from 3D reconstructed data. We use Cartesian control to simplify domain transfer. Since log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limit on computational resources and time for the challenge of image segmentation, and allows for collecting data in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2--5 logs.Comment: 8 pages, 10 figure

Predictor models for high-performance wheel loading

Autonomous wheel loading involves selecting actions that maximize the total performance over many repetitions. The actions should be well adapted to the current state of the pile and its future states. Selecting the best actions is difficult since the pile states are consequences of previous actions and thus are highly unknown. To aid the selection of actions, this paper investigates data-driven models to predict the loaded mass, time, work, and resulting pile state of a loading action given the initial pile state. Deep neural networks were trained on data using over 10,000 simulations to an accuracy of 91-97,% with the pile state represented either by a heightmap or by its slope and curvature. The net outcome of sequential loading actions is predicted by repeating the model inference at five milliseconds per loading. As errors accumulate during the inferences, long-horizon predictions need to be combined with a physics-based model.Comment: 22 pages, 19 figure

Wavefront reconstruction of discontinuous phase objects from optical deflectometry

One of the challenges in phase measuring deflectometry is to retrieve the wavefront from objects that present discontinuities or non-differentiable gradient fields. Here, we propose the integration of such gradients fields based on an Lp-norm minimization problem. The solution of this problem results in a nonlinear partial differential equation, which can be solved with a fast and well-known numerical methods and doesn't depend on external parameters. Numerical reconstructions on both synthetic and experimental data are presented that demonstrate the capability of the proposed method

Continuous control of an underground loader using deep reinforcement learning

Reinforcement learning control of an underground loader is investigated in simulated environment, using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of the pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point, while avoiding collisions, getting stuck, or losing ground traction. It relies on motion and force sensors, as well as on camera and lidar. Using a soft actor-critic algorithm the agents learn policies for efficient bucket filling over many subsequent loading cycles, with clear ability to adapt to the changing environment. The best results, on average 75% of the max capacity, are obtained when including a penalty for energy usage in the reward.Comment: 9 pages, 7 figure