Ammar: complex military terrain autonomy
Recognising the transformative potential of AI and autonomy, Dstl has funded a PhD student in the USA to conduct cutting-edge research in this field.
The Defence Science and Technology Laboratory (Dstl) has long been at the forefront of advancing defence technologies.
Now, for the first time, Dstl has funded a PhD student in the USA to carry out artificial intelligence (AI) and autonomy research.
This initiative highlights the collaboration between Dstl and the USA to explore the objectives, progress, and potential impact of this research on both academia and UK defence.
About the project
The 3-year funded project with Texas A&M University will focus on researching autonomy in complex land environments in 3 key areas:
- improvement in autonomous navigation and behavioural algorithms for unmanned ground vehicles (UGVs) in unstructured and dynamic environments
- create and refine UGV awareness and navigation features optimised for challenging off-road and urban cluttered environments at speeds exceeding 40 kilometres per hour
- develop UGV amphibious models and vessels for mixed testing with specific emphasis on water obstacles
The work will enable the advancement of autonomy on land and sea and will see the development of new models for testing.
The collaboration with the Texas A&M University J. Mike Walker ‘66 Department of Mechanical Engineering has strengthened the UK-US relationship. It provides access to their world-class facilities, while harnessing the power of working with Dstl’s engineers and scientists.
Ethics
Ethical considerations in AI and autonomy are integral to the safe and responsible development of technologies. Given the significant impact these systems can have, particularly in defence applications, it’s important to ensure they are developed with stringent ethical guidelines to prevent misuse and unintended consequences. This involves not only adhering to existing regulatory frameworks but also actively participating in the dialogue around ethical AI to continuously re-assess and update standards. Ethical AI practices not only safeguard human rights but also increase the trust and reliability of autonomous systems in critical applications.
About the PHD student
Ammar is a first-year mechanical engineering doctoral student at the Texas A&M University, specialising in mobile robotics and control systems. He’s developing autonomous systems capable of adaptive learning in complex environments, which is a critical area for future defence applications.
His previous experience has included contributing to the design and manufacturing of collaborative robots, exoskeletons and active prostheses, and he’s also experienced in utilising motion capture systems for human biomechanical analysis.
Dstl’s funding will enable Ammar to fully immerse himself in his PhD research from the outset. As a Graduate Assistant Research (GAR), he is able to devote all his time to advancing his research, free from teaching duties. The funding also facilitates extensive experiments at Texas A&M’s StarLab facility on the RELLIS Campus.
By the end of his research, Ammar aims to validate an advanced autonomy architecture that effectively bridges the simulation-to-real-world gap for autonomous ground systems. This innovation will significantly improve the reliability and functionality of UGVs in unstructured and dynamic environments, specifically in military applications where such capabilities are necessary.
Key research questions
- How can autonomous systems be designed to improve learning efficiency in real-time?
- What algorithms can be developed to enhance the decision-making capabilities of these systems?
Ammar said:
The most exciting aspect of the research is its potential to enhance autonomous technologies in both defence and civilian sectors.
By developing cutting-edge autonomy architectures for unstructured environments, I am uniquely positioned to impact the future of unmanned ground vehicle technology.
The extensive resources available from Texas A&M and Dstl enable me to tackle new challenges every day, making the work both engaging and rewarding.
Ammar employs a combination of machine learning techniques, sensor integration, and real-world testing to develop and validate his models. The approach involves iterative testing and refinement, ensuring that the systems can adapt to various operational scenarios.
What Ammar has achieved so far
Since Ammar’s PhD program began, he’s made significant strides in developing autonomous systems. Major turning points include the successful implementation of adaptive learning algorithms and the completion of preliminary field tests demonstrating the systems’ capabilities in real-world scenarios.
Ammar’s research has revealed that autonomous systems can achieve substantial improvements in learning efficiency through the integration of advanced sensor technologies and real-time data processing. These findings have the potential to revolutionise the deployment of autonomous systems in defence operations.
About the funding and research
Dstl’s funding initiative is part of a broader effort to harness global talent and advance strategic research areas. The funding provided to Ammar includes financial support for his research activities, access to specialised equipment, and opportunities for collaboration with leading experts in the field. This partnership aims to benefit the UK by bridging the gap between US and UK academic research for practical defence applications.
The collaboration between Dstl and the US exemplifies the transformative potential of strategic funding and support in advancing critical research areas. By investing in the development of autonomous systems with adaptive learning capabilities, Dstl is not only enhancing defence technologies but also contributing to the broader field of AI research. As Ammar’s research progresses, it holds promise for ground-breaking innovations that could redefine the future of autonomous systems in defence and beyond.
More about the research
The research supported by Dstl is poised to make a significant impact on both the academic and defence sectors. In the short term, the advancements in adaptive learning for autonomous systems can enhance the effectiveness of defence operations, reducing the need for human intervention in hazardous environments. Long-term implications include the broader adoption of these technologies in various defence applications, potentially leading to a shift in military strategy and operations.
Beyond defence, Ammar’s research contributes to the academic body of knowledge, providing insights that can be leveraged by other researchers and practitioners in the field of AI and autonomy.
More about AI and autonomy
AI and autonomy are vital to the future of defence in both the UK and the USA; acting as major components in modern military strategies. The evolving landscape of global conflicts (notably the ongoing situation in Ukraine) underscores the urgent need for advanced autonomous systems in both land and air defence systems.
These technologies not only enhance operational effectiveness but also increase safety by reducing the need for human soldiers in high-risk environments. AI and autonomy therefore drive innovation and strategic advantage in defence sectors.
Ammar’s research contributes to a small but pivotal part in advancing the role of AI and autonomy in future defence applications.
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