UAV Mission Monitoring and Sequencing

Goudarzi, Hirad; Richards, Arthur

doi:10.1109/icuas48674.2020.9213870

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…In [6] a simulated autonomous driving system for behaviors such as overtaking and obstacle avoidance is presented. BTs are also utilised to control UAVs and swarms of UAVs [7]. [8] used BTs for intermediate level control to execute task planning and actuating of micro aerial vehicles.…”

Section: Related Work a Behavior Trees(bts)mentioning

confidence: 99%

A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving

Yi¹,

Nebot²

2021

Preprint

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Fig. 1. Vehicle trajectories of 53 rosbags in the University of Sydney Campus Dataset output from the GPS+lidar main filter are superimposed together onto a satellite image. No manual alignment of trajectory is performed. The color of trajectory points indicates the state of update used by the filter. Green: lidar feature updating. Blue: dead reckoning. Red: GPS updating. The blue dead reckoning area in the middle of the image is an underground carpark.

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Section: Related Work a Behavior Trees(bts)mentioning

confidence: 99%

A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving

Yi¹,

Nebot²

2021

Preprint

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Skeptical Dynamic Dependability Management for Automated Systems

Arnez

Ollier

Radermacher

et al. 2022

2022 25th Euromicro Conference on Digital System Design (DSD)

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Semi-autonomous drone control with safety analysis

Goudarzi

Richards

2023

Drone Syst. Appl.

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This paper describes the partial automation of drones (also referred to as uncrewed air vehicles, UAVs or aerial robots) in a populated area within the visual line-of-sight of its pilot. Mission responsiveness is improved by reducing the number of human crew members and avoiding the need for area clearance, while carefully managing the workload of those remaining to ensure no compromise on safety. The work employs a system-centric approach with regards to integrating human and automation tasks based on their capabilities and use of standard procedures whilst prioritizing the predictability and simplicity of the overall system. Safety claims about the proposed system are posed and rigorously analyzed through a structured safety case. The proposed system is applied to a bridge inspection case study with simulation results and scenario analysis.

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UAV Mission Monitoring and Sequencing

Cited by 3 publications

References 10 publications

A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving

A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving

Skeptical Dynamic Dependability Management for Automated Systems

Semi-autonomous drone control with safety analysis

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