Edwin Kreuzer

Learning a deep neural net policy for end-to-end control of autonomous vehicles

Deep neural networks are frequently used for computer vision, speech recognition and text processing. The reason is their ability to regress highly nonlinear functions. We present an end-to-end controller for steering autonomous vehicles based on a convolutional neural network (CNN). The deployed framework does not require explicit hand-engineered algorithms for lane detection, object detection or path planning. The trained neural net directly maps pixel data from a front-facing camera to steering commands and does not require any other sensors. We compare the controller performance with the steering behavior of a human driver.

HippoCampus: A micro underwater vehicle for swarm applications

The HippoCampus platform is a low-cost micro autonomous underwater vehicle for swarm robotics research. This paper presents the hardware and software design, the communication link, instrumentation, and control system. The quadrotor design enables the vehicle to perform agile maneuvers in a very confined test tank. Vehicle navigation is based on the on-board sensor suite. Autonomous path following is presented and experimental results are evaluated.

Embedded Spherical Localization for Micro Underwater Vehicles Based on Attenuation of Electro-Magnetic Carrier Signals

Self-localization is one of the most challenging problems for deploying micro autonomous underwater vehicles (μAUV) in confined underwater environments. This paper extends a recently-developed self-localization method that is based on the attenuation of electro-magnetic waves, to the μAUV domain. We demonstrate a compact, low-cost architecture that is able to perform all signal processing steps present in the original method. The system is passive with one-way signal transmission and scales to possibly large μAUV fleets. It is based on the spherical localization concept. We present results from static and dynamic position estimation experiments and discuss the tradeoffs of the system.

Learning environmental fields with micro underwater vehicles: a path integral—Gaussian Markov random field approach

Autonomous underwater vehicles (AUVs) are advancing the state of the art in numerous scientific and commercial applications. The current surge in micro electronics enables the development of small micro AUVs (

Towards a hyperbolic acoustic one-way localization system for underwater swarm robotics

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Adaptive Fuzzy Sliding Mode Controller and Observer for a Dive Cell

μAUVs) which are expected to gain increasing popularity in industrial applications such as monitoring of liquid-based processes. This paper presents an information theoretic approach for exploration and monitoring of liquid containing tanks with

Reinforcement Learning of Depth Stabilization with a Micro Diving Agent

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