Twin delayed deep deterministic policy gradient-based target tracking for unmanned aerial vehicle with achievement rewarding and multistage training
Target tracking using an unmanned aerial vehicle (UAV) is a challenging robotic problem. It
requires handling a high level of nonlinearity and dynamics. Model-free control effectively …
requires handling a high level of nonlinearity and dynamics. Model-free control effectively …
Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems
The paper introduces an interactive machine learning mechanism to process the
measurements of an uncertain, nonlinear dynamic process and hence advise an actuation …
measurements of an uncertain, nonlinear dynamic process and hence advise an actuation …
An adaptive multi-level quantization-based reinforcement learning model for enhancing UAV landing on moving targets
The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an
essential functionality in various UAV-based applications. It can be added to a teleoperation …
essential functionality in various UAV-based applications. It can be added to a teleoperation …
A Survey of Offline-and Online-Learning-Based Algorithms for Multirotor Uavs
Multirotor UAVs are used for a wide spectrum of civilian and public domain applications.
Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or …
Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or …
An online model-following projection mechanism using reinforcement learning
In this article, we propose a model-free adaptive learning solution for a model-following
control problem. This approach employs policy iteration, to find an optimal adaptive control …
control problem. This approach employs policy iteration, to find an optimal adaptive control …
[HTML][HTML] A Vision-Based End-to-End Reinforcement Learning Framework for Drone Target Tracking
X Zhao, X Huang, J Cheng, Z Xia, Z Tu - Drones, 2024 - mdpi.com
Drone target tracking, which involves instructing drone movement to follow a moving target,
encounters several challenges:(1) traditional methods need accurate state estimation of …
encounters several challenges:(1) traditional methods need accurate state estimation of …
Aerial interception of non-cooperative intruder using model predictive control
R Srivastava, R Lima, K Das - 2022 American Control …, 2022 - ieeexplore.ieee.org
Autonomous capture of an unknown non-cooperative aerial target is a complex task
requiring real-time target localization, trajectory prediction and control. The current work …
requiring real-time target localization, trajectory prediction and control. The current work …
A data-driven model-reference adaptive control approach based on reinforcement learning
Model-reference adaptive systems refer to a consortium of techniques that guide plants to
track desired reference trajectories. Approaches based on theories like Lyapunov, sliding …
track desired reference trajectories. Approaches based on theories like Lyapunov, sliding …
Integrated targeting, guidance, navigation, and control for unmanned aerial vehicles
E Kawamura - 2020 - search.proquest.com
The goal of this dissertation research is to demonstrate the integration of targeting,
guidance, navigation, and control (TGNC) functions for real-time implementation onboard …
guidance, navigation, and control (TGNC) functions for real-time implementation onboard …
Range estimation and visual servoing of a dynamic target using a monocular camera
R Srivastava, A Maity, R Lima… - … Conference on Unmanned …, 2020 - ieeexplore.ieee.org
This paper delves into the problem of tracking an unknown maneuvering target using only
monocular visual feedback. It is usually difficult to perform target tracking using only …
monocular visual feedback. It is usually difficult to perform target tracking using only …