Dr. Alessandro Gardi

Novel Hybrid Methods for Multi-Objective Optimization of Aerospace Vehicle Trajectories

This project aims to develop new computationally-efficient hybrid algorithms to determine globally optimal solutions for a wide variety of aerospace trajectory optimization problems formulated as multi-objective and multi-phase optimal control problems. These hybrid algorithms fill the gap in the current state-of-the-art space trajectory optimization methods, which include highly accurate and computationally efficient pseudospectral methods on one side and globally optimal metaheuristic methods on the other side. The algorithms are applied to a set of contemporary aerospace vehicles in representative mission scenarios around the United Arab Emirates (UAE). The primary target for implementation of the proposed hybrid method is a Multidisciplinary Design Optimization (MDO) framework in a closed-loop design architecture. The implementation in a prototype 4D trajectory Planning and Negotiation/Validation (4-PNV) system for strategic and tactical online trajectory planning in shared airspace is also pursued.

Project partners: RMIT University, Australian DoD/DST Group and SmartSat CRC

Intelligent Health and Mission Management for Trusted Autonomous Aerospace Vehicles

Capitalizing on the findings from recent PhD projects and industry/government collaborations in Australia on this topic, this project aims to develop Intelligent Health and Mission Management (IHMM) systems, which exploit Digital Twin, Artificial Intelligence (AI) and suitable onboard sensor networks to ensure operational safety in increasingly autonomous aerospace vehicles. The developed IHMM systems shall replace the conventional flight crew in detecting the onset of anomalies and in planning and enacting reversionary actions, as without these critical IHMM capabilities an autonomous aerospace vehicle would be prone to catastrophic failures. The project will focus on the integrity of the most important safety-critical systems in contemporary aerospace vehicles, such as: UAS sense-and-avoid systems, electric power and propulsion systems and flight control systems for eVTOL and satellites. The developed IHMM system prototype will be experimentally verified in laboratory and ground/flight tests exploiting the available university facilities.

Project partners: RMIT University, SmartSat CRC, Australian DoD/DST Group, PHM Technology Pty Ltd, Insitech Pty Ltd

Development of Remote Sensing Systems for Smart Agriculture from UAS and Satellites

Considerable volumes of agricultural produce fail to meet the market’s quality expectations and is sold for a lower price or rejected. In order to meet market preferences and overcome supply chain challenges, the agricultural sector need to produce goods that satisfy consumer preference for size, taste, colour and texture; meet export market access protocols for air and sea freight; and that can survive periods of sub-optimal storage. Smart agriculture technology is therefore needed to track and predict diseases, produce quality attributes and harvest timing. This project aims to demonstrate that innovative remote sensing systems can be integrated on UAS and satellite platforms to enable product quality prediction and harvest optimization.

Project partners: RMIT University, FoodAgility CRC and SmartSat CRC