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AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
TL;DR: A new simulator built on Unreal Engine that offers physically and visually realistic simulations for autonomous vehicles in real world and that is designed from the ground up to be extensible to accommodate new types of vehicles, hardware platforms and software protocols.
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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 (e.g. 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.
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Citations
Sense-and-avoid system development on an FPGA
Fulop Kota,Tamas Zsedrovits,Zoltán Nagy +2 more
- 01 Jun 2019
TL;DR: Fundamental considerations for the selection of tools used during the development of a collision avoidance system for UAV are introduced, and the aim is to realize the same algorithm with higher framerate and lower power consumption.
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Integrated Framework for Fast Prototyping and Testing of Autonomous Systems
TL;DR: A modular hardware-in-the-loop development simulation framework that allows realistic simulation, supporting multi-vehicle scenario and comprehending tools for reproducing realistic testing environments with advanced sensors is presented.
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A Collective Adaptive Approach to Decentralised k-Coverage in Multi-robot Systems
TL;DR: A new take on the online multi-object k-coverage problem (OMOkC), where mobile robots are required to sense a mobile target from k diverse points of view, coordinating themselves in a scalable and possibly decentralised way, is proposed.
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The vulnerability of UAVs: an adversarial machine learning perspective
Michael Doyle,Joshua D. Harguess,Keith Manville,Mikel Rodriguez +3 more
- 22 Apr 2021
TL;DR: This work describes a methodology for understanding the vulnerability of UAVs to these attacks by threat modeling each potential state and mode of the UAV, from powering-on, to various mission modes and examines one potential threat vector.
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Trilateration Positioning Using Hybrid Camera-LiDAR System
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