Online identification of a two-mass system in frequency domain using a Kalman filter

Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies

Industrial applications of the Kalman filter:a review

International audienc

Mobile robot localization using a Kalman filter and relative bearing measurements to known landmarks

This paper discusses mobile robot localization using a single, fixed camera that is capable of detecting predefined landmarks in the environment. For each visible landmark, the camera provides a relative bearing but not a relative range. This research represents work toward an inexpensive sensor that could be added to a mobile robot in order to provide more accurate estimates of the robot\u27s location. It uses the Kalman filter as a framework, which is a proven method for incorporating sensor data into navigation problems. In the simulations presented later, it is assumed that the filter can perform accurate feature recognition. In the experimental setup, however, a webcam and an open source library are used to recognize and track bearing to a set of unique markers. Although this research requires that the landmark locations be known, in contrast to research in simultaneous localization and mapping, the results are still useful in an industrial setting where placing known landmarks would be acceptable

Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer

This paper develops a novel slip estimator using the invariant observer design theory and Disturbance Observer (DOB). The proposed state estimator for mobile robots is fully proprioceptive and combines data from an inertial measurement unit and body velocity within a Right Invariant Extended Kalman Filter (RI-EKF). By embedding the slip velocity into $\mathrm{SE}_3(3)$ Lie group, the developed DOB-based RI-EKF provides real-time accurate velocity and slip velocity estimates on different terrains. Experimental results using a Husky wheeled robot confirm the mathematical derivations and show better performance than a standard RI-EKF baseline. Open source software is available for download and reproducing the presented results.Comment: github repository at https://github.com/UMich-CURLY/slip_detection_DOB. arXiv admin note: text overlap with arXiv:1805.10410 by other author

産業用ロボットの振動要素を考慮した先端加速度に基づくモーション制御法に関する研究

国立大学法人長岡技術科学大

Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets

Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization

In this study, we address multi-robot localization issues, with a specific focus on cooperative localization and observability analysis of relative pose estimation. Cooperative localization involves enhancing each robot's information through a communication network and message passing. If odometry data from a target robot can be transmitted to the ego robot, observability of their relative pose estimation can be achieved through range-only or bearing-only measurements, provided both robots have non-zero linear velocities. In cases where odometry data from a target robot are not directly transmitted but estimated by the ego robot, both range and bearing measurements are necessary to ensure observability of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. We compare extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation using different robust loss functions (filtering and smoothing with varying batch sizes of sliding windows) in terms of estimation accuracy. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, applying both EKF and PGO and comparing their performance.Comment: 20 pages, 21 figure