Zhang Sujie, Hou Qianru, Zhang Xiaoyang, Wu Xu, Wang Hongpeng
Tianjin College, University of Science & Technology Beijing, Tianjin 301830, China.
Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
Sensors (Basel). 2023 Aug 21;23(16):7305. doi: 10.3390/s23167305.
Unmanned vehicles frequently encounter the challenge of navigating through complex mountainous terrains, which are characterized by numerous unknown continuous curves. Drones, with their wide field of view and ability to vertically displace, offer a potential solution to compensate for the limited field of view of ground vehicles. However, the conventional approach of path extraction solely provides pixel-level positional information. Consequently, when drones guide ground unmanned vehicles using visual cues, the road fitting accuracy is compromised, resulting in reduced speed. Addressing these limitations with existing methods has proven to be a formidable task. In this study, we propose an innovative approach for guiding the visual movement of unmanned ground vehicles using an air-ground collaborative vectorized curved road representation and trajectory planning method. Our method offers several advantages over traditional road fitting techniques. Firstly, it incorporates a road star points ordering method based on the K-Means clustering algorithm, which simplifies the complex process of road fitting. Additionally, we introduce a road vectorization model based on the piecewise GA-Bézier algorithm, enabling the identification of the optimal frame from the initial frame to the current frame in the video stream. This significantly improves the road fitting effect (EV) and reduces the model running time (-model). Furthermore, we employ smooth trajectory planning along the "route-plane" to maximize speed at turning points, thereby minimizing travel time (-travel). To validate the efficiency and accuracy of our proposed method, we conducted extensive simulation experiments and performed actual comparison experiments. The results demonstrate the superior performance of our approach in terms of both efficiency and accuracy.
无人驾驶车辆经常面临在复杂山区地形中导航的挑战，这些地形的特点是有许多未知的连续弯道。无人机具有广阔的视野和垂直移动的能力，为弥补地面车辆有限的视野提供了一种潜在的解决方案。然而，传统的路径提取方法仅提供像素级的位置信息。因此，当无人机使用视觉线索引导地面无人驾驶车辆时，道路拟合精度会受到影响，导致速度降低。用现有方法解决这些限制已被证明是一项艰巨的任务。在本研究中，我们提出了一种创新方法，用于使用空地协作的矢量化弯道道路表示和轨迹规划方法来引导地面无人驾驶车辆的视觉运动。我们的方法相对于传统的道路拟合技术具有几个优点。首先，它采用了基于K均值聚类算法的道路星点排序方法，简化了复杂的道路拟合过程。此外，我们引入了基于分段GA - 贝塞尔算法的道路矢量化模型，能够在视频流中从初始帧到当前帧识别最优帧。这显著提高了道路拟合效果（EV）并减少了模型运行时间（-model）。此外，我们沿着“路线平面”采用平滑轨迹规划，以在转弯点最大化速度，从而最小化行驶时间（-travel）。为了验证我们提出的方法的效率和准确性，我们进行了广泛的模拟实验并进行了实际比较实验。结果证明了我们的方法在效率和准确性方面的卓越性能。
