Abstract
This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensors. For the outdoor environment, the Extended Kalman Filter (EKF) is used to estimate the robot position and orientation, the system consists of wheel encoders, a reduced inertial sensor system (RISS), and a Global Positioning System (GPS). For the indoor environment, where GPS signals are blocked, another EKF algorithm is proposed using low cost depth sensor (Microsoft Kinect stream). EKF indoor localization is based on landmarks extracted from the depth measurements. A hybrid low-cost reduced navigation system (HLRNS) for indoor and outdoor environments is proposed and validated in both simulation and experimental environments. Additionally, an input-output state feedback linearization (I-O SFL) technique is used to control the robot to track the desired trajectory in such an environment. According to the conducted validation simulation and experimental testing, the proposed HLRNS provides an acceptable performance to be deployed for real-time applications.
Highlights
The aim of this paper is to provide a comprehensive solution to the navigation problem of Mobile Robots, by developing a robust technique that enables robots to face the numerous challenges arising in indoor and outdoor environments
The robot starts from an outdoor environment where the pose estimate is obtained using Extended Kalman Filter (EKF), which combines Global Positioning System (GPS), compass and encoder measurements
The hybrid localization algorithm should switch to the indoor algorithm that relies on landmarks that are placed on the corners
Summary
The aim of this paper is to provide a comprehensive solution to the navigation problem of Mobile Robots, by developing a robust technique that enables robots to face the numerous challenges arising in indoor and outdoor environments. This research focuses on analyzing localization techniques using commercially available sensors with the integration of trajectory tracking algorithm and with its implementation on the Wheeled Mobile Robot (WMR). Mobile robot navigation covers a broad spectrum of Mechatronic systems. In mobile robot navigation systems, there are two types of localization sensors. The first type is the onboard sensor which includes encoders and inertial measurement units (IMUs). These sensors measure the robot’s linear and angular velocities and acceleration along the robot’s body axes
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