The future challenge for field robots is to increase the level of autonomy towards long distance (>1 km) and duration (>1h) applications. One of the key technologies is the ability to accurately estimate the properties of the traversed terrain to optimize onboard control strategies and energy efficient path-planning, ensuring safety and avoiding possible immobilization conditions that would lead to mission failure. Two main hypotheses are put forward in this research. The first hypothesis is that terrain can be effectively detected by relying exclusively on the measurement of quantities that pertain to the robot-ground interaction, i.e., on proprioceptive signals. Therefore, no visual or depth information is required. Then, artificial deep neural networks can provide an accurate and robust solution to the classification problem of different terrain types. Under these hypotheses, sensory signals are classified as time series directly by a Recurrent Neural Network or by a Convolutional Neural Network in the form of higher-level features or spectrograms resulting from additional processing. In both cases, results obtained from real experiments show comparable or better performance when contrasted with standard Support Vector Machine with the additional advantage of not requiring an a priori definition of the feature space.

野外机器人的未来挑战是提高远距离（> 1 km）和持续时间（> 1h）应用的自主水平。关键技术之一是能够准确估计穿越地形的特性，以优化机载控制策略和节能路径规划，确保安全并避免可能导致任务失败的固定条件。本研究提出了两个主要假设。第一个假设是地形可以通过完全依赖于与机器人-地面交互相关的量的测量，即本体感受信号来有效地检测。因此，不需要视觉或深度信息。然后，人工深度神经网络可以为不同地形类型的分类问题提供准确而稳健的解决方案。在这些假设下，感觉信号直接被循环神经网络或卷积神经网络以更高级别的特征或额外处理产生的频谱图的形式分类为时间序列。在这两种情况下，与标准支持向量机相比，从实际实验中获得的结果显示出可比或更好的性能，并且具有不需要先验定义特征空间的额外优势。