Abstract
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.
Highlights
Today, we can almost say that robots are used in all areas of human life
While implementing inertial navigation system (INS) (IMU), the system was helped by camera data for
The errors robot atofthe facingand the charger began to presented find the shortest path to the charger statistical analysis based on mean square error (MSE)
Summary
We can almost say that robots are used in all areas of human life. From the military, industrial and even domestic fields, robots are deployed in all those fields, and these deployments continue to increase every day. Kalman filter (UKF) in various domains nowadays, ranging from target tracking [15] to multi-sensor fusion [16,17] Another form of sensor fusion research to improve the performance of existing mobile robots is found in [18], where two methods (Dempster–Shafer theory and Kalman filtering) are used to integrate a global positioning system (GPS) and an inertial measurement unit (IMU), and the obtained results allowed for selecting the most accurate method for robot localization at an appropriate cost. The navigation systems of the inertial navigation system (INS), Odom-NIRNA and the KF-based estimation system are combined to develop a new estimation approach, based on a hybridization technique named hierarchical infrared navigational algorithm hybridization (HIRNAH), to improve the accuracy of the current estimation systems for four-wheeled mobile robot (4-WMR) localization.
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