English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Currently, almost all robot state estimation and localization systems are based on the Kalman filter (KF) and its derived methods, in particular the unscented Kalman filter (UKF). When applying the UKF alone, the estimate of the state is not sufficiently precise. In this paper, a new hierarchical infrared navigational algorithm hybridization (HIRNAH) system is developed to provide better state estimation and localization for mobile robots. Two navigation subsystems (inertial navigation system (INS) and, using a novel infrared navigation algorithm (NIRNA), Odom-NIRNA) and an RPLIDAR-A3 scanner cooperation to build HIRNAH. The robot pose (position and orientation) errors are estimated by a system filtering module (SFM) and used to smooth the robot’s final poses. A prototype (two rotary encoders, one smartphone-based robot sensing model and one RPLIDAR-A3 scanner) has been built and mounted on a four-wheeled mobile robot (4-WMR). Simulation results have motivated real-life experiments, and obtained results are compared to some existent research (hardware and control technology navigation (HCTNav), rapid exploring random tree (RRT) and in stand-alone mode (INS)) for performance measurements. The experimental results confirm that HIRNAH presents a more accurate estimation and a lower mean square error (MSE) of the robot’s state than those calculated by the previously cited HCTNav, RRT and INS.

State Estimation and Localization Based on Sensor Fusion for Autonomous Robots in Indoor Environment