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Found 10 papers, 1 papers with code
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife.
Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach
In this paper we propose a framework towards achieving two intertwined objectives: (i) equipping reinforcement learning with active exploration and deliberate information gathering, such that it regulates state and parameter uncertainties resulting from modeling mismatches and noisy sensory; and (ii) overcoming the huge computational cost of stochastic optimal control.
UWB Ranging and IMU Data Fusion: Overview and Nonlinear Stochastic Filter for Inertial Navigation
The filter is designed to mimic the nonlinear navigation motion kinematics and is posed on a matrix Lie Group, the extended form of the Special Euclidean Group $\mathbb{SE}_{2}\left(3\right)$.
An Observer-Based Reinforcement Learning Solution for Model-Following Problems
The first strategy observes the states of desired process dynamics, while the second one stabilizes and optimizes the closed-loop system.
Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion
The discrete version of the proposed observer is tested using a publicly available real-world dataset of a drone flight.
Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators
In this work, a novel Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers.
An Online Model-Following Projection Mechanism Using Reinforcement Learning
This approach employs policy iteration, to find an optimal adaptive control solution.
Adaptive Neural Network Stochastic-Filter-based Controller for Attitude Tracking with Disturbance Rejection
Secondly, a novel control law on $SO(3)$ coupled with the proposed estimator is presented.
Neural-adaptive Stochastic Attitude Filter on SO(3)
This paper proposes a novel stochastic nonlinear neural-adaptive-based filter on $SO(3)$ for the attitude estimation problem.
Temporal-Logic-Based Intermittent, Optimal, and Safe Continuous-Time Learning for Trajectory Tracking
In this paper, we develop safe reinforcement-learning-based controllers for systems tasked with accomplishing complex missions that can be expressed as linear temporal logic specifications, similar to those required by search-and-rescue missions.
