A disaster robot is used for crucial rescue, observation, and exploration missions. In the case of implementing robots in bad environmental situations, must be equipped with appropriate sensors good algorithms to carry out expected movements. this study, a neural network-based terrain classification that applied Raspberry using IMU sensor as input developed. Relatively low computational require...

Given a 1.5-dimensional terrain T , also known as an x-monotone polygonal chain, the Terrain Guarding problem seeks a set of points of minimum size on T that guards all of the points on T . Here, we say that a point p guards a point q if no point of the line segment pq is strictly below T . The Terrain Guarding problem has been extensively studied for over 20 years. In 2005 it was already estab...

3D terrain data have been used for various systems such as navigation and landscape analysis systems. Recently, the number of 3D terrain data and databases is increasing rapidly. To handle these data effective, such systems need the ability to search 3D terrains to find matches based on shape similarity. In this paper, we propose a pattern matching technique for searching similar terrain segmen...

Legged robots are popular candidates for missions in challenging terrains due to their versatile locomotion strategies. Terrain classification is a key enabling technology autonomous legged robots, allowing them harness innate flexibility adapt the demands of operating environment. We show how highly capable machine learning techniques, namely gated recurrent neural networks, allow our target r...

Mobile robots operating in unknown urban environments encounter a wide range of complex terrains to which they must adapt their planned trajectory for safe and efficient navigation. Most existing approaches utilize supervised learning classify from either an exteroceptive or proprioceptive sensor modality. However, this requires tremendous amount manual labeling effort each newly encountered te...

In this paper, we address the problem of terrain classification using a technically blind hexapod walking robot. The proposed approach is built on top of the existing method based on analysis of the feedback from the robot’s actuators and the desired trajectory. The formed method uses features for the Support Vector Machine classification method that assumes a regular time-invariant gait to con...

This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by our approach can be used for path planning, navigation, ...