This research program investigates how to reliably make autonomous decisions and control for robots in the presence of uncertainty. We are achieving this by advancing model-based filtering, estimation, detection, decision and planning techniques for robotic systems.
Our research questions include how to:
- design robotic systems capable of achieving specified performance outcomes in the presence of uncertainty
- detect anomalous system conditions in weak measurement environments
- characterise and manage network systems.
Projects
- Aerial Manipulation for Remote Sensing Applications
- Aerial Mapping of Forests Affected by Pathogens using UAVs, Hyperspectral Sensors and Artificial Intelligence: Myrtle Rust
- Aerospace Autonomy
- AOS: Kelpie
- Novel autonomous robotic weed control to maximise agricultural productivity
- Assessing the capabilities of digital imaging and Unmanned Aerial Systems (UAS) for species management
- Autonomous Mission Planning, Navigation and Geological Feature Recognition using UAVs (Drones)
- Autonomous UAV decision making under environment and target detection uncertainty
- Detection and mapping of exotic weeds using UAS and machine learning: Bitou Bush Case Study
- Developing pest risk models of Buffel Grass using Unmanned Aerial Systems and Statistical methods
- Development and validation of a UAV based system for air pollution measurements
- Establishing advanced networks for air quality sensing and analysis
- LunaRoo - A hopping Lunar science platform
- Monitor of pollutants adjacent to a motorway using Unmanned Aerial Vehicles
- Multi-UAV Navigation in GPS-Denied Environments under Location and Environmental Uncertainty
- Multiple Target Finding and Action Using Unmanned Aerial Systems
- Navigating Under the Forest Canopy and in the Urban Jungle
- UAVs/ Drones for Agriculture and Plant Biosecurity
- Semi-automated power pole inspection
- Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation
- UAV Navigation using semantic cues
- UAVs, Hyperspectral Remote Sensing and Machine learning Revolutionizing Reef Monitoring
- UAVs with slung/swung loads
- Use of UAS and Hyperspectral Remote Sensing for Early detection of Phylloxera Infestation in Vineyards
- When every second counts: Multi-drone navigation in GPS-denied environments
Team
Led by Professor Jason Ford.
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Dr Aaron Mcfadyen
Lecturer
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Dr Christina Kazantzidou
Lecturer
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Prof Daniel Quevedo
Professor
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Assoc Prof Felipe Gonzalez
Associate Professor
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Guilherme Fróes Silva
PhD Researcher
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Dr Jasmin Martin
Lecturer
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Prof Jason Ford
Program Lead (Decision & Control), Professor
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Jenna Riseley
Research Assistant
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Juan Sandino
PhD Researcher
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Justin Kennedy
Research Assistant
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Assoc Prof Luis Mejias Alvarez
Associate Professor
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Matthew Cooper
PhD Researcher
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Dr Troy Bruggemann
Senior Lecturer
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Valtteri Kallinen
PhD Researcher
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Xiaolong Zhu
PhD Researcher