Using multiple sensors for autonomous mobile robot navigation
This work addresses navigation challenges for autonomous mobile robots in domestic environments, but it appears incremental as it combines existing methods like Lyapunov functions and Kalman filters.
The paper tackles autonomous mobile robot navigation in houses by using a multi-sensor system for 3D image acquisition and mapping, resulting in algorithms for dimensionality reduction, trajectory design, and obstacle avoidance, with sensor integration via an extended Kalman filter to identify robot location and orientation despite environmental interference.
This paper presents the use of multi-sensor measurement system to guide autonomous mobile robot in the house. The system allows the 3D image acquisition to global mapping, and algorithms to reduce the dimensionality of images to 2D global map navigation, trajectory design approach using the Lyapunov function method and avoid obstacles by the potential energy can also be presented. Also, sensor integrated method based on extended Kalman filter allows us to identify the exact location and orientation of the robot in the presence of interference from the environment.