Artificial intelligence and machine learning for autonomous military vehicles

Vecherin, Sergey N.; Desmond, Jacob; Hodgdon, Taylor S.; Bates, Jordan; Parker, Michael; Lever, J. H.; Hoch, Garett; Bodie, Mark; Shoop, Sally A.

doi:10.21079/11681/37943

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Driving on different types of terrain exhibits different behaviour. Operating in these unstructured off-road environments, the agent needs to analyze the terrain traversability and plan an optimal and terrain-aware path according to it [1] [3]. Traversability analysis is defined as estimating the difficulty of driving through terrain for an off-road ground vehicle [2].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Terrain Traversability Analysis in Off-road Environments

Leung

Ignatyev

Zolotas

2022

2022 8th International Conference on Automation, Robotics and Applications (ICARA)

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There is a significant growth in autonomy level in off-road ground vehicles. However, unknown off-road environments are often challenging due to their unstructured and rough nature. To find a path that the robot can move smoothly to its destination, it needs to analyse the surrounding terrain. In this paper, we present a hybrid terrain traversability analysis framework. Semantic segmentation is implemented to understand different types of the terrain surrounding the robot; meanwhile geometrical properties of the terrain are assessed with the aid of a probabilistic terrain estimation. The framework represents the traversability analysis on a robot-centric cost map, which is available to the path planners. We evaluated the proposed framework with synchronised sensor data captured while driving the robot in real off-road environments. This thorough terrain traversability analysis will be crucial for autonomous navigation systems in off-road environments.

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Section: Introductionmentioning

confidence: 99%

Hybrid Terrain Traversability Analysis in Off-road Environments

Leung

Ignatyev

Zolotas

2022

2022 8th International Conference on Automation, Robotics and Applications (ICARA)

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Deep Learning in Military Applications

Surma

2024

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The latest advancements in Artificial Intelligence, especially in Deep Learning technology, accelerate innovation and development in different application domains. The development of Deep Learning technology has profoundly impacted military development trends, leading to major changes in the forms and models of war. In this paper, we overview Deep Learning's history and architecture. Then, we review related work and extensively describe Deep Learning in two primary military applications: intelligence operations and autonomous platforms. Finally, we discuss related threats, opportunities, technical and practical difficulties. The main findings are that Artificial Intelligence technology is not omnipotent and needs to be applied carefully, considering its limitations, cybersecurity threats and a strong need for human supervision in the OODA decision loop. Certain safeguard mechanisms are required at the strategic decision-making level. In this context, one of the most important aspects relates to the education, training and selection of military officer personnel.

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AI-Enhanced Proactive Model Predictive Control for Off-Road Autonomous Vehicles

Bhosale,

Gupta,

Kumar

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"> <div class="htmlview paragraph">This paper investigates the use of artificial intelligence (AI) to enhance autonomous vehicle (AV) controls in unstructured off-road environments. Recent research developments address the utilization of AI and deep learning (DL) models to share information with AVs, enabling intelligent navigation in urban environments. The suggested research allows AVs to navigate in an off-road environment by utilizing AI-predicted trajectories while avoiding obstacles. Our method aims to enhance vehicle autonomy, enabling it to move promptly and responsively while interacting with other vehicles in an off-road environment. We demonstrate the efficacy of AI-driven trajectory predictions through comprehensive simulation validations. Moreover, the research demonstrates the use of AI prediction to construct a proactive Model Predictive Controller (MPC) for off-road autonomous vehicles. Simulation studies and comparisons with existing approaches validate the effectiveness and advantages of the proposed approach for off-road autonomous vehicle controls.</div> </div>

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Artificial intelligence and machine learning for autonomous military vehicles

Cited by 3 publications

References 13 publications

Hybrid Terrain Traversability Analysis in Off-road Environments

Hybrid Terrain Traversability Analysis in Off-road Environments

Deep Learning in Military Applications

AI-Enhanced Proactive Model Predictive Control for Off-Road Autonomous Vehicles

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