Decision and control

  • Decision and control

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

Network-Informed Control – Control-Informed network

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

Unmanned Traffic Management (UTM)

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.