Map Construction and Path Planning Method for Mobile Robots Based on Collision Probability Model

Abstract

A map construction method based on a collision probability model and an improved A* algorithm is proposed to address the issues of insufficient security in mobile robot map construction and path planning in complex environments. The method is based on modeling the asymmetry of paths, which complicates problem solving. Firstly, this article constructs a collision probability function model, and based on this model it is fused with the obstacle grid map, which is based on the grid method, to draw a collision probability grid map (CPGM) containing collision probability information. Secondly, incorporating the collision probability values from the CPGM into the actual cost function of the traditional A* algorithm improves the security of path planning in complex environments. The experimental results show that the improved A* algorithm decreases the percentage of dangerous nodes in complex environments by 69.23%, shortens the path planning length by 19.52%, reduces the search time by 16.8%, and reduces the number of turns by 46.67%. Therefore, the method in this paper solves the problem of traditional grid maps lacking security information and can plan a path with higher security and which is smoother, improving the security and robustness of mobile robot autonomous navigation in complex environments.

The application of extended kalman filtering based on SLAM

“…The prediction observation calculation was completed, and the matching process of road signs was realized by using the prediction observation and observation measurement. The state update uses the Kalman filter principle to realize the pose estimation of mobile robots, and its essence is to determine the confidence degree of the predicted pose and the pose corresponding to the observation road sign [13]. The following are the simulation results: match with the straight-line features obtained by using the state model to predict pose, and then complete the estimation process of actual pose by the Kalman filter algorithm.…”

“…Figure 6. Attitude error of a mobile robot[13]. In view of the precise positioning requirements of mobile robots in the carriage, LiDAR is used to obtain environmental point cloud information to extract and observe straight line features, and then…”

Scheduling of Multi-AGV Systems in Automated Electricity Meter Verification Workshops Based on an Improved Snake Optimization Algorithm

“…6. In previous studies, the paths of individual AGVs were considered to be asymmetric due to the influence of various factors such as orders, complex scenarios, and algo rithm differences [31]. To study the path planning and motion control of AGVs in complex factory environments, this paper abstracts and models an automated electric meter verification workshop as a grid map.…”