Showing papers presented at "Field and Service Robotics in 2017"

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

Abstract: Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process Also, in order to utilize recent advances in machine intelligence and deep learning we need to collect a large amount of annotated training data in a variety of conditions and environments We present a new simulator built on Unreal Engine that offers physically and visually realistic simulations for both of these goals Our simulator includes a physics engine that can operate at a high frequency for real-time hardware-in-the-loop (HITL) simulations with support for popular protocols (eg MavLink) The simulator is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols In addition, the modular design enables various components to be easily usable independently in other projects We demonstrate the simulator by first implementing a quadrotor as an autonomous vehicle and then experimentally comparing the software components with real-world flights

Sensing Water Properties at Precise Depths from the Air.

Abstract: Water properties critical to our understanding and managing of freshwater systems change rapidly with depth. This work presents an Unmanned Aerial Vehicle (UAV) based method of keeping a passive, cable-suspended sensor payload at a precise depth, with \(95\%\) of submerged sensor readings within \(\pm 8.4\,\text {cm}\) of the target depth, helping dramatically increase the spatiotemporal resolution of water science datasets. We use a submerged depth altimeter attached at the terminus of a \(3.5\,\text {m}\) semi-rigid cable as the sole input to a depth controller actuated by the UAV’s motors. First, we simulate the system and common environmental disturbances of wind, water, and GPS drift and then use parameters discovered during simulation to guide implementation. In field experiments, we compare the depth precision of our new method to previous methods that used the UAV’s altitude as a proxy for submerged sensor depth, specifically: (1) only using the UAV’s air-pressure altimeter; and (2) fusing UAV-mounted ultrasonic sensors with the air-pressure altimeter. Our new method reduces the standard deviation of depth readings by \(75\%\) in winds up to \(8\,\text {m/s}\). We show the step response of the depth-altimeter method when transitioning between target depths and show that it meets the precision requirements. Finally, we explore a longer, \(8.0\,\text {m}\) cable and show that our depth-altimeter method still outperforms previous methods and allows scientists to increase the spatiotemporal resolution of water property datasets.