Abstract
In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized.
Highlights
With the rapid development of computer science, sensor technology, and other hightech industries, mobile robot technology is developing rapidly
In order to solve the path planning problem of intelligent vehicles in unknown environments, we designed a method of map construction and path planning for mobile robots based on multi-sensor information fusion
According to the new obstacle information obtained by the vehicle sensor in real time, integrate the information into the grid map, and use the dynamic window method to complete the local obstacle avoidance
Summary
With the rapid development of computer science, sensor technology, and other hightech industries, mobile robot technology is developing rapidly. Wang et al proposed a hybrid path planning algorithm based on the A* algorithm and the dynamic window method This method solves the problem of path planning in a complex multi-objective environment and improves the efficiency of autonomous navigation [14]. In order to solve the path planning problem of intelligent vehicles in unknown environments, we designed a method of map construction and path planning for mobile robots based on multi-sensor information fusion. (2) A hybrid path planning method is proposed on the basis of the combination of the improved ant colony algorithm and the dynamic window method Using this method, the path turning angle can be optimized, the collision with obstacles can be avoided, the efficiency of path planning is improved, and the automatic feedback mechanism of a mobile robot is realized
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