中圖分類號(hào)：TP242 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.233781 中文引用格式： 權(quán)鈺涵，張嘯，劉冬，等. 融合激光SLAM實(shí)現(xiàn)平衡車智能導(dǎo)航[J]. 電子技術(shù)應(yīng)用，2023，49(10)：141-147. 英文引用格式： Quan Yuhan，Zhang Xiao，Liu Dong，et al. Implementation of the intelligent navigation of balance vehicle with laser SLAM[J]. Application of Electronic Technique，2023，49(10)：141-147.
Implementation of the intelligent navigation of balance vehicle with laser SLAM
Quan Yuhan1，Zhang Xiao2，Liu Dong2，3，Luo Rui2，3，He Yun2，3
(1.College of Automation， Shenyang Aerospace University， Shenyang 110136， China； 2.Shenyang Institute of Automation， Chinese Academy of Sciences， Shenyang 110016， China； 3.Institutes for Robotics and Intelligent Manufacturing， Chinese Academy of Sciences， Shenyang 110169， China)
Abstract： Based on the existing two-wheeled intelligent balance car in China, it has almost no autonomous obstacle avoidance and positioning functions. In order to improve its safety and flexibility, laser SLAM technology is applied to the traditional self-balancing car to realize autonomous mapping and path planning, positioning and obstacle avoidance. The Kalman filter is used to fuse the acceleration and tilt angle collected by the six-axis sensor. In terms of mapping and positioning, the Cartographer algorithm released by Google is used, and the Teb algorithm under move base integrated by the Navigation function package is used for path planning and obstacle avoidance. Since the speed of the vehicle is relatively slow and the jitter needs to be avoided as much as possible when mapping the lidar, it is very important to keep the vehicle model in a stable motion state. For this purpose, the data obtained by the sensor is firstly filtered, and then the PID parameters of the car are fine-tuned. At the same time, for more convenient control, the bluetooth function is added to control the movement of the car through bluetooth to achieve rapid map building. After adding SLAM technology, traditional self-balancing scooters can realize obstacle avoidance and positioning functions, detect static and dynamic obstacles in real time, and plan an optimal route around obstacles, realizing the function of unmanned driving.
Key words : remote sensing；sensor；SLAM mapping and navigation；Cartographer；Bluetooth remote control；Kalman filter；Teb algorithm
