FLC tuned with Gravitational Search Algorithm for Nonlinear Pose Filter

TL;DR

This paper presents a novel nonlinear pose filter tuned with a gravitational search algorithm, enhancing robustness and convergence speed in pose estimation under uncertain conditions.

Contribution

It introduces a fuzzy rule-based online tuning method for nonlinear pose filters, optimized via graphical search algorithm for improved performance.

Findings

Demonstrates robustness under uncertain measurements.

Achieves fast convergence in pose estimation.

Effective in large initial errors.

Abstract

Nonlinear pose (\textit{i.e,} attitude and position) filters are characterized with simpler structure and better tracking performance in comparison with other methods of pose estimation. A critical factor when designing a nonlinear pose filter is the selection of the error function. Conventional design of nonlinear pose filter design trade-off between fast adaptation and robustness. This paper introduces a new practical approach based on fuzzy rules for on-line continuous tuning of the nonlinear pose filter. Each of input and output membership functions are optimally tuned using graphical search algorithm optimization considering both pose error and its rate of change. The proposed approach is characterized with high adaptation features and strong level of robustness. Therefore, the proposed approach results of robust and fast convergence properties. The simulation results show the effectiveness of the proposed approach considering uncertain measurements and large error in initialization.