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Research Vision
The Autonomous and Intelligent Systems Laboratory (AIS Lab) seeks to address challenging societal issues through continuous contributions in the following research areas.
- Operation of unmanned/autonomous systems in complex environments.
- Collaboration among multi-network unmanned systems.
- Development leveraging information-based approaches.
System & Control
- The focus of autonomy research is shifting from traditional hardware to software, specifically algorithms.
- The AIS Lab is dedicated to innovation and contributions in the field of autonomy research, with a strong emphasis on the algorithmic aspect.
- The primary research areas include planning, acting, and perception, etc.
Related Projects
- Control Allocation Algorithms, funded by BAE Systems, 2022-2022
- Last Mile Delivery of non-GNSS resilient time, frequency and synchronisation (TFS), funded by Innovate UK, 2021-2022
- Sensor Synchronisation for CUAS and UTM applications, funded by Innovate UK, 2021-2022
- Powerplant Integration of Novel Engine Systems (PINES), funded by Innovate UK, 2019-2021
- SAVANA - Satcom and VHF Architectures for Nextgen Avionics, funded by Thales, 2018-2020
- UAV Inspection of Aircraft Wing, funded by Airbus, 2018-2019
- Human Drive , funded by Innovate UK, 2017-2019
Multi-Agent System (MAS)
- MAS (Multi-Agent Systems) offer advantages that cannot be achieved with Single Agent Systems, such as increased flexibility, enhanced reliability and resilience, and the ability to cover large areas simultaneously.
- Efficient collaboration in MAS is a critical element for successful operations, with decision-making serving as the key enabler of such collaboration.
- AIS Lab conducts research based on mathematical optimization and numerical approaches to design and analyze collaborative decision-making in MAS.
- This ensures that MAS not only achieves mission success but also responds predictably to operator commands.
Related Projects
- SAFIR - Obtain Flight Mission Readiness, Enabling Rapid Intervention for Healthcare and Critical Infrastructure, Leveraging All Value Chain Actors and U-Space Services, funded by SESAR, 2023-2026
- A Study on Operation Procedure of UAM/AAM Take-Off/Landing at Vertiport Adjacent to Airport, funded by IAIAC, 2023-2023
- Project BLUEPRINT - Developing a Blueprint for Scalable UTM-Enabled BVLOS Drone Operations Across the UK, funded by Innovate UK, 2022-2024
- Project HADO - High intensity Autonomous Drone Operations project, funded by Innovate UK, 2022-2024
- Project SafeZone Phase 3, funded by Innovate UK, 2022-2024
- EuroDRONE – A European UTM testbed for U-space, funded by SESAR, 2018-2020
- EuroDRONE – A European UTM testbed for U-space, funded by SESAR, 2018-2020
- Fully automated operation of three drones in VLOS (Visual Line of Sight) and two drones in BVLOS (Beyond Visual Line of Sight) using DronAssistant
Information-Driven Approaches
- Autonomous systems are inherently AI systems and must handle increasing amounts of data.
- AIS Lab focuses on integrating cutting-edge artificial intelligence technologies and big data into its research areas.
- Future mobility must withstand not only predictable failures but also unforeseen breakdowns to meet the required efficiency and reliability.
- The design of such systems achieves higher availability by combining redundant hardware and software.
- AIS Lab has initiated research to explore computationally efficient and fully analytical methods related to these challenges, playing a leading role both theoretically and experimentally.
Related Projects
- LANDOne - Towards Artificial Intelligence Enabled Landing Gear with Trustworthy Autonomy, funded by ATI, 2023-2026
- Adaptive flight control to enhance survivability & availability, funded by BAE Systems, 2019-2022
- Real-time Decision Making for Autonomous Systems, funded by US Air Force, 2019-2022
- Trajectory Optimisation using Reinforcement Learning, funded by Inha University, 2019-2020
- AIRMES - Airline Maintenance Operations implementation of an E2E Maintenance Service Architecture and its enablers, funded by Horizon 2020, 2016-2019
