Event based distributed kalman filter for limited resource multirobot cooperative localization

This paper is concerned with the mobile robot localization problem subject to filter gain uncertainty under dynamic event-triggered communication mechanism, and meanwhile, the H ∞ filtering performance and the error variance constraint are guaranteed. For saving the sensor energy, a dynamic event-triggered communication mechanism is introduced to manage the transmission of the measurement data from the sensor to the filter. To characterize the possible fluctuations of the desired filter gain, a resilient filter is constructed for the mobile robot localization. The aim of this paper is to find a solution to the mobile robot localization problem by designing a nonlinear resilient filter such that the filtering error dynamics satisfies both the H ∞ performance requirement and the error variance constraint over a finite time horizon simultaneously. By resorting to the Lyapunov theory and the stochastic analysis technique, the sufficient conditions are established to guarantee that the error dynamic system satisfies both the H ∞ performance requirement and the error variance constraint. Then, a recursive linear matrix inequality (RLMI) approach is employed to design the desired filter. Based on the proposed filter design scheme, the corresponding localization algorithm is presented. Finally, an experiment is conducted in the simulation environment to verify the effectiveness of the proposed localization algorithm. KEYWORDS dynamic event-triggered communication mechanism, error variance constraint, H ∞ filtering, mobile robot localization, nonlinear resilient filter This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Section: Introductionmentioning

confidence: 99%

Variance‐constrained resilient H_∞ filtering for mobile robot localization under dynamic event‐triggered communication mechanism

Karimi

2021

“…In many robotic and sensing applications, object‐localization problem is a crucial and indispensable issue [1–5], and acoustic or sonar sensors based on ultrasonic propagation are widely used to obtain reasonable and accurate geometric information about the location of objects near the robot [6–12]. There are two key points for the quality of ultrasonic sensing results in localization technology.…”

Section: Introductionmentioning

confidence: 99%

Single‐object localization using multiple ultrasonic sensors and constrained weighted least‐squares method

Juan

2021

In this paper, an active single‐object localization system with U‐shaped ultrasonic module array is presented. The proposed algorithm in this system has two stages. The first stage is time‐of‐flight (TOF) estimation of reflected ultrasonic signals. We analyze several distortion cases of reflection signals and provide solutions to improve the performance of TOF estimation. The second stage is TOF‐based object localization. A good localization algorithm can suppress the problems caused by inaccurate TOF estimation and improve the accuracy of target positioning. We propose two object‐localization algorithms to estimate the object location. One is the unconstrained least‐squares method, and the other is the constrained weighted least‐squares method based on the eigenvalues‐analyzing technique. The simulation results suggest that the performance of the proposed constrained weighted method is better than that of the unconstrained method. In addition, the median smoother and the outlier exclusion scheme are implemented in the overall system to make the object location estimation more stable. The performance of the proposed system is confirmed by the experiment results, which show that the single‐object localization has a considerable degree of accuracy and stability.

“…Recently, decentralized optimization has been extensively studied owing to its wide applications in the fields of cooperative control [1–5], sensor networks [6–8], machine learning [9–11], economic power dispatch [12,13], and state estimation [14]. Clearly, decentralized optimization is robust than traditional centralized optimization in terms of communication networks, and various practical problems can be modeled as decentralized optimization, where m agents cooperatively resolve the following optimization problem:

\min_{\overset{x}{true} \in ℝ^{n}} \overset{f}{true} ((), \overset{x}{true}) = \frac{1}{m} true \sum_{i = 1}^{m} {\overset{f}{true}}_{i} ((), \overset{x}{true}),

where

\overset{x}{true} \in ℝ^{n}

is the decision vector and each agent in the network has the knowledge of only one local objective function,

{\overset{f}{true}}_{i} : ℝ^{n} \to ℝ

.…”

Section: Introductionmentioning

confidence: 99%

A decentralized Nesterov gradient method for stochastic optimization over unbalanced directed networks

Xia

Cheng

et al. 2020

Decentralized stochastic gradient methods play significant roles in large‐scale optimization that finds many practical applications in machine learning and coordinated control. This paper studies optimization problems over unbalanced directed networks, where the mutual goal of agents in the network is to optimize a global objective function expressed as a sum of local objective functions. Each agent using only local computation and communication in the networks is assumed to get access to a stochastic first‐order oracle. In order to devise a noise‐tolerant decentralized algorithm with accelerated linear convergence, a decentralized Nesterov gradient algorithm with the constant step‐size and parameter using stochastic gradients is proposed in this paper. The proposed algorithm employing a gradient‐tracking technique is proved to converge linearly to an error ball around the optimal solution via the analysis on a linear system when the positive constant step‐size and parameter are sufficiently small. We further recover the exact linear convergence for the proposed algorithm with exact gradients under the same selection conditions of the constant step‐size and parameter. Some real‐world data sets are used in simulations to validate the correctness of the theoretical findings and practicability of the proposed algorithm.