Outdoor Navigation Method of Mobile Robot Based on Model Predictive Control
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摘要: 针对户外巡检、户外清洁、智能农业等户外场景对自主机器人的使用需求，设计了一种具有很强实时性和稳定性的移动机器人户外导航方法。移动机器人收到户外GPS航迹点后，使用激光雷达实时获取周边环境点云并构建局部栅格图，在栅格地图内使用基于路段走向改进的A-star算法搜索局部避障路径，最后设计使用模型预测控制器以跟踪避障轨迹。为了验证该导航方法的可行性，在仿真和户外环境下分别进行了对比实验，实验结果表明所生成的轨迹稳定平滑并能有效避障，模型预测控制器轨迹跟踪精度高、耗时短，实现了户外移动机器人高效率、稳定导航。Abstract: Recent years have witnessed autonomous outdoor robots have prevailed. The outdoor navigation is more difficult than the indoor one because the outdoor environment is more complicated. Various methods for outdoor navigation were proposed. Vision-based methods are vulnerable to weather, road marker-based methods lack flexibility and machine learning-based methods are unable to cope with the complex outdoor environments. A navigation method is proposed in this paper, which includes a real-time local grid map constructer, road direction-based trajectory planner, and model predictive control-based tracking controller. During the navigation task, the point cloud of the surrounding environment is obtained through a 16 thread radar, and the local grid map is constructed in real time. The mission goal is transformed to the robot coordinates, then the improved A-star algorithm based on the road direction is used to search the local obstacle avoidance path. Finally, a differential robot model predictive controller is designed to track the trajectory. Both simulation and experimental results are presented and discussed. It is revealed in the simulation tests that the designed planner can ensure collision avoidance with fewer turns and the designed motion controller has good tracking performance, which helps to reduce total mission execution time. It is shown in the outdoor experiment that the navigation method drives the robot to preselected mission point in turn with a stable motion. The distance between the robot and the obstacle is more than 1 meter in the process of obstacle avoidance. Furthermore, there is no need to build a map in advance, the proposed method is more applicable and efficient in various outdoor environments.
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表 1 MPC控制器参数
Table 1. Related parameters of MPC controller
$ {N}_{c} $/step $ {N}_{p} $/step $ {T}_{0} $/s $ {W}_{1} $ $ {W}_{2} $ $ {W}_{3} $ 10 10 0.15 15 8 5 
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表 2 规划及耗时参数
Table 2. Planning and time consuming parameters
Consumed time for planning/s Path length/m Number of turns Consumed time for tracking/s Improved $ {A}^{*} $ 0.17 115.12 16 243 Dijkstra 0.39 113.37 20 281
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