Enhancing Situational Awareness of Helicopter Pilots in Unmanned Aerial Vehicle-Congested Environments Using an Airborne Visual Artificial Intelligence Approach.

The use of drones or Unmanned Aerial Vehicles (UAVs) and other flying vehicles has increased exponentially in the last decade. These devices pose a serious threat to helicopter pilots who constantly seek to maintain situational awareness while flying to avoid objects that might lead to a collision. In this paper, an Airborne Visual Artificial Intelligence System is proposed that seeks to improve helicopter pilots' situational awareness (SA) under UAV-congested environments.

Reservoir Computing for Drone Trajectory Intent Prediction: A Physics Informed Approach.

The design of accurate trajectory prediction algorithms is crucial to implement adequate countermeasures against drones with anomalous performances. Wrong predictions may cause high-false-positives that compromise safety in national infrastructures. In this article, a physics informed reservoir computing (PIRC) scheme for drone trajectory prediction is proposed.

Drone's Objective Inference Using Policy Error Inverse Reinforcement Learning.

Drones are set to penetrate society across transport and smart living sectors. While many are amateur drones that pose no malicious intentions, some may carry deadly capability. It is crucial to infer the drone's objective to prevent risk and guarantee safety.

Hippocampus experience inference for safety critical control of unknown multi-agent linear systems.

Risk mitigation is usually addressed in simulated environments for safety critical control. The migration of the final controller requires further adjustments due to the simulation assumptions and constraints. This paper presents the design of an experience inference algorithm for safety critical control of unknown multi-agent linear systems.

A Closed-Loop Output Error Approach for Physics-Informed Trajectory Inference Using Online Data.

While autonomous systems can be used for a variety of beneficial applications, they can also be used for malicious intentions and it is mandatory to disrupt them before they act. So, an accurate trajectory inference algorithm is required for monitoring purposes that allows to take appropriate countermeasures. This article presents a closed-loop output error approach for trajectory inference of a class of linear systems.

A complementary learning approach for expertise transference of human-optimized controllers.

In this paper, a complementary learning scheme for experience transference of unknown continuous-time linear systems is proposed. The algorithm is inspired in the complementary learning properties that exhibit the hippocampus and neocortex learning systems via the striatum. The hippocampus is modelled as pattern-separated data of a human optimized controller.

Optimal sliding mode control for cutting tasks of quick-return mechanisms.

A solution of the constant cutting velocity problem of quick-return mechanisms is the main concern of this paper. An optimal sliding mode control in the task space is used to achieve uniform and accurate cuts throughout the workpiece. The switching hyperplane is designed to minimize the position error of the slider-dynamics in an infinite horizon.

Neural H₂ Control Using Continuous-Time Reinforcement Learning.

In this article, we discuss continuous-time H control for the unknown nonlinear system. We use differential neural networks to model the system, then apply the H tracking control based on the neural model. Since the neural H control is very sensitive to the neural modeling error, we use reinforcement learning to improve the control performance.

Discrete-Time H Neural Control Using Reinforcement Learning.

In this article, we discuss H control for unknown nonlinear systems in discrete time. A discrete-time recurrent neural network is used to model the nonlinear system, and then, the H tracking control is applied based on the neural model. Since this neural H control is very sensitive to the neural modeling error, we use reinforcement learning and another neural approximator to improve tracking accuracy and robustness of the controller.