Nonlinear moving horizon state estimation for multi-robot relative localization

Abstract

This paper presents a novel approach for a multirobot system's relative localization (RL), where one or more robots are located and tracked with respect to another robot frame of reference. With a known initial estimate of a robot being tracked, the extended Kalman filter (EKF) has been shown to perform adequately well to achieve the RL. However, with an arbitrary initial estimate, EKF performance may become unstable and/or require a high number of iterations to achieve an acceptable tracking error. In this paper, moving horizon estimation (MHE) has been adopted to achieve the RL objective. Although MHE has been highlighted in the literature to be computationally intractable, in this work, an efficient algorithm based on Real Time Iteration (RTI) scheme has been exported using an automatic C code generation toolkit. The exported code is adapted for the RL problem and requires computational capacity of the order ones of milliseconds. The MHE performance is compared against the EKF in numerical simulations. Under arbitrary estimator initialization, the results confirms that MHE over performs EKF in terms of the number of iterations required for convergence while satisfying the real-time requirements.

Nonlinear Model Predictive Growth Control of a Class of Plant-Inspired Soft Growing Robots

“…Besides, a robustness analysis has been conducted based on Monte-Carlo simulations to evaluate the vine robot growth under various disturbance conditions as well as to guide the direction of choosing the weighting matrices in the control problem in such a way to maximize the tracking performance. In future work, building a Moving Horizon Estimation (MHE) [18] is promising to relax the assumption of full state observability that has been assumed in this research. Also, our work would be possibly extended to a case wherein the dynamics model of vine growing robots is used instead of the kinematics model either a prediction model or as the model of the process under control.…”

A combined robust approach based on auto‐regressive long short‐term memory network and moving horizon estimation for state‐of‐charge estimation of lithium‐ion batteries

“…The estimation window moves iteratively as the current time step moves forward with physical time changing. 41 When the time step is one step forward, the next time step will become the current time step accordingly. In the novel estimation window N, the oldest measured and predicted values will be discarded, and the new estimated value from the observer in the novel current time step will be added in the following state estimation process.…”

A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

“…Therein, a dual antenna GNSS, two encoders, and an IMU are used to localize the robot while estimating the slip error components. MHE was used in several studies for localization: Mehrez et al (2014) used nonlinear MHE for the relative localization of a multi-robot system. An efficient algorithm based on realtime iteration scheme was used to improve the computational efficiency.…”