Unmanned vehicle dynamic obstacle avoidance method based on particle filtering

Abstract

The invention discloses an unmanned vehicle dynamic obstacle avoidance method based on particle filtering. An unmanned vehicle is provided with a master control system, and a motion control system, an inertial navigation system, a laser radar module and an IMU module which are respectively connected with the master control system. A map and unmanned vehicle operating system ROS control software are loaded in the master control system; the unmanned vehicle judges the speed and the position of the unmanned vehicle through an IMU module, scans the surrounding environment of the unmanned vehicle through a laser radar, compares a picture obtained through scanning with map information, and firstly removes known obstacles on a map; sampling the remaining obstacles by adopting a particle filtering mode, and predicting the movement speed and the movement trend of the obstacles relative to the unmanned vehicle; a DWA algorithm is combined with an obstacle particle filtering mode to avoid moving obstacles; and when the distance between the unmanned vehicle and the moving obstacle reaches a safe distance, the unmanned vehicle continues to move along the global optimal path.

Description

technical field [0001] The invention relates to the technical field of unmanned vehicles, in particular to a particle filter-based dynamic obstacle avoidance method for unmanned vehicles. Background technique [0002] With the rapid development of unmanned vehicle-related technologies, people's demand for unmanned vehicles is getting higher and higher, especially the positioning technology of unmanned vehicles. The positioning technology of unmanned vehicles usually provides the pose (including position and heading angle) of the unmanned vehicle in the world coordinate system in the environment. At the same time, the positioning technology of unmanned vehicles is the key technology for unmanned vehicles to realize autonomous navigation. [0003] For the global positioning of unmanned vehicles in the preset environment map, the existing method is to use particle filter positioning technology to locate mobile unmanned vehicles. The adaptive Monte Carlo algorithm of particle f...

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Problems solved by technology

[0004] However, the particle filter pose estimation of the existing adaptive Monte Carlo positioning algorithm has the following disadvantages: 1. When the current local environment of the unmanned vehicle has changed greatly compared with the preset static map local area, the current observation There are differences between the data and the preset static local environment. Matching the existing preset static local environment and then adjusting the particle weight will lead to pose estimation errors; 2. When the mileage displacement data has obvious errors, the estimated pose will also be corresponding Obvious error occurred

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