Institute of Automatic Control and Robotics, Warsaw University of Technology, 02-525 Warsaw, Poland.
Łukasiewicz Research Network-Industrial Research Institute for Automation and Measurements PIAP, 02-486 Warsaw, Poland.
Sensors (Basel). 2022 Dec 29;23(1):383. doi: 10.3390/s23010383.
The article presents the application of a hyperspectral camera in mobile robot navigation. Hyperspectral cameras are imaging systems that can capture a wide range of electromagnetic spectra. This feature allows them to detect a broader range of colors and features than traditional cameras and to perceive the environment more accurately. Several surface types, such as mud, can be challenging to detect using an RGB camera. In our system, the hyperspectral camera is used for ground recognition (e.g., grass, bumpy road, asphalt). Traditional global path planning methods take the shortest path length as the optimization objective. We propose an improved A* algorithm to generate the collision-free path. Semantic information makes it possible to plan a feasible and safe path in a complex off-road environment, taking traveling time as the optimization objective. We presented the results of the experiments for data collected in a natural environment. An important novelty of this paper is using a modified nearest neighbor method for hyperspectral data analysis and then using the data for path planning tasks in the same work. Using the nearest neighbor method allows us to adjust the robotic system much faster than using neural networks. As our system is continuously evolving, we intend to examine the performance of the vehicle on various road surfaces, which is why we sought to create a classification system that does not require a prolonged learning process. In our paper, we aimed to demonstrate that the incorporation of a hyperspectral camera can not only enhance route planning but also aid in the determination of parameters such as speed and acceleration.
本文介绍了高光谱相机在移动机器人导航中的应用。高光谱相机是一种成像系统，能够捕捉广泛的电磁光谱。与传统相机相比，这一特性使它们能够检测更广泛的颜色和特征，并更准确地感知环境。一些表面类型，如泥，使用 RGB 相机很难检测到。在我们的系统中，高光谱相机用于地面识别（例如草、凹凸不平的道路、沥青）。传统的全局路径规划方法以最短路径长度作为优化目标。我们提出了一种改进的 A*算法来生成无碰撞路径。语义信息使得在复杂的越野环境中规划可行和安全的路径成为可能，以行驶时间作为优化目标。我们展示了在自然环境中采集的数据的实验结果。本文的一个重要创新点是使用修改后的最近邻方法进行高光谱数据分析，然后在同一工作中使用数据进行路径规划任务。使用最近邻方法可以使我们能够比使用神经网络更快地调整机器人系统。由于我们的系统在不断发展，我们打算在各种路面上检查车辆的性能，这就是为什么我们寻求创建一个不需要长时间学习过程的分类系统。在本文中，我们旨在证明高光谱相机的引入不仅可以增强路线规划，还可以帮助确定速度和加速度等参数。
