Navigation is an important research interest for mobile robot running indoor and remote outdoor environment with weak GPS signal. There are three issues to realize autonomous navigation, namely, localization, mapping and path planning. An improved particle filtering algorithm based on Bayesian filtering theory is proposed to solve simultaneous localization and mapping (SLAM) problem, which establishes the grid map around the unknown environment. A control decision scheme combining global path planning and local dynamic obstacle avoidance is proposed for mobile robot navigation, while A* algorithm and dynamic window approach (DWA) are applied to optimize the whole path and local dynamic path respectively. To reduce system error, the least square method is applied to calibrate the odometer and the piecewise linear interpolation method is used to suppress the distortion of lidar. Finally, the simulation is carried out in the Gazebo environment under the ROS architecture, which shows the path planned in grid map is safe and smooth. The experimental results show that the proposed scheme is effective, that is, the autonomous navigation method has high accuracy and good consistency with the strong dynamic obstacle avoidance ability.
Autonomous Navigation for Mobile Robot Based on a Fusion Algorithm
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 131 ; 1307-1319
2022-03-18
13 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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