针对无人机在未知环境中的实时避障,提出一种局部规划方法。该方法根据传感器实时探测到的障碍点信息,随时构建出一个狄利克雷边值问题。采用有限差分法求解该问题,即得到一个局部地图的拉普拉斯势场。随着传感器信息的更新,不断更换新构建的势场。这种构建势场的方法对各种障碍物形态适应程度高,且势场中不存在局部极小点。以势场的负梯度方向作为参考方向,并以此生成参考速度,采用 PID 控制器进行速度跟踪以实现无人机的自主导航。最后,使用 MATLAB 进行不同场景下的仿真实验,结果表明本方法可以有效实现无人机在不同未知环境下的实时避障导航。
This paper proposes a local planning method for real-time obstacle avoidance in unknown environments. This method constructs a Dirichlet boundary value problem once the obstacle points are obtained from sensors. This problem is solved by FDM (finite difference method), and hence it generates a Laplacian potential field based on the local map. The potential field is replaced by a new one when the sensing data got updated. This construction can efficiently deal with complex environments, and there is no local minimum in the field. The reference velocities are generated by the directions of the negative gradient in the field, which are tracked by the PID controller, in order to achieve autonomous UAV (unmanned aerial vehicle)navigation. Finally, MATLAB experiments are taken under different scenes, and the result shows this method is valid for real-time obstacle avoidance in different unknown environments.
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