An Intent Modeling and Inference Framework for Autonomous and Remotely Piloted Aerial Systems

• URL: http://arxiv.org/abs/2409.08472v1
• Date: Fri, 13 Sep 2024 01:57:37 GMT
• Title: An Intent Modeling and Inference Framework for Autonomous and Remotely Piloted Aerial Systems
• Authors: Kesav Kaza, Varun Mehta, Hamid Azad, Miodrag Bolic, Iraj Mantegh,
• Abstract summary: An intent modelling and inference framework is presented to assist the defense planning for protecting a geo-fence against unauthorized flights. The concepts of critical waypoints and critical waypoint patterns are introduced and associated with a motion process to fully characterize an intent. It is applicable to autonomous, semi-autonomous, and piloted systems in 2D and 3D environments with obstacles.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An intent modelling and inference framework is presented to assist the defense planning for protecting a geo-fence against unauthorized flights. First, a novel mathematical definition for the intent of an uncrewed aircraft system (UAS) is presented. The concepts of critical waypoints and critical waypoint patterns are introduced and associated with a motion process to fully characterize an intent. This modelling framework consists of representations of a UAS mission planner, used to plan the aircraft's motion sequence, as well as a defense planner, defined to protect the geo-fence. It is applicable to autonomous, semi-autonomous, and piloted systems in 2D and 3D environments with obstacles. The framework is illustrated by defining a library of intents for a security application. Detection and tracking of the target are presumed for formulating the intent inference problem. Multiple formulations of the decision maker's objective are discussed as part of a deep-learning-based methodology. Further, a multi-modal dynamic model for characterizing the UAS flight is discussed. This is later utilized to extract features using the interacting multiple model (IMM) filter for training the intent classifier. Finally, as part of the simulation study, an attention-based bi-directional long short-term memory (Bi-LSTM) network for intent inference is presented. The simulation experiments illustrate various aspects of the framework, including trajectory generation, radar measurement simulation, etc., in 2D and 3D environments.

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