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Found 17 papers, 0 papers with code
MPC Based Linear Equivalence with Control Barrier Functions for VTOL-UAVs
In this work, we propose a cascaded scheme of linear Model prediction Control (MPC) based on Control Barrier Functions (CBF) with Dynamic Feedback Linearization (DFL) for Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs).
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.
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.
Exponentially Stable Observer-based Controller for VTOL-UAVs without Velocity Measurements
A full state observer for the estimation of VTOL-UAV motion parameters (attitude, angular velocity, position, and linear velocity) is proposed on the Lie Group of $\mathbb{SE}_{2}\left(3\right)\times\mathbb{R}^{3}$ $=\mathbb{SO}\left(3\right)\times\mathbb{R}^{9}$ with almost globally exponentially stable closed loop error signals.
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.
Nonlinear Deterministic Filter for Inertial Navigation and Bias Estimation with Guaranteed Performance
Unmanned vehicle navigation concerns estimating attitude, position, and linear velocity of the vehicle the six degrees of freedom (6 DoF).
Stochastic Observer for SLAM on the Lie Group
A robust nonlinear stochastic observer for simultaneous localization and mapping (SLAM) is proposed using the available uncertain measurements of angular velocity, translational velocity, and features.
Asymptotically Stable Observer-based Controller for Attitude Tracking with Systematic Convergence
This paper proposes a novel unit-quaternion observer-based controller for attitude tracking (attitude and angular velocity) with guaranteed transient and steady-state performance.
Geometric Stochastic Filter with Guaranteed Performance for Autonomous Navigation based on IMU and Feature Sensor Fusion
A computationally cheap geometric nonlinear stochastic navigation filter is proposed on $\mathbb{SE}_{2}(3)$ with guaranteed transient and steady-state performance.
A Geometric Nonlinear Stochastic Filter for Simultaneous Localization and Mapping
Keywords: Simultaneous Localization and Mapping, nonlinear stochastic observer for SLAM, stochastic differential equations, pose estimator, position, attitude, Brownian motion process, inertial measurement unit, landmarks, features, SDE, SO(3), SE(3), SLAM.
Nonlinear Filter for Simultaneous Localization and Mapping on a Matrix Lie Group using IMU and Feature Measurements
Simultaneous Localization and Mapping (SLAM) is a process of concurrent estimation of the vehicle's pose and feature locations with respect to a frame of reference.
Pose Estimation
Simultaneous Localization and Mapping
Robotics
Systems and Control
Systems and Control
Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias
Keywords: Simultaneous Localization and Mapping, Nonlinear filter for SLAM, Nonlinear filter for SLAM on Matrix Lie group, pose, asymptotic stability, prescribed performance, adaptive estimate, feature, inertial measurement unit, inertial vision unit, IMU, SE(3), SO(3), noise.
Fast Adaptation Nonlinear Observer for SLAM
The process of simultaneously mapping the environment in three dimensional (3D) space and localizing a moving vehicle's pose (orientation and position) is termed Simultaneous Localization and Mapping (SLAM).
Nonlinear Attitude Filter on SO(3): Fast Adaptation and Robustness
The conventional design of nonlinear attitude filters has a trade-off between fast adaptation and robustness.
