Vision-Based Terrain Classification and Solar Irradiance Mapping for Solar-Powered Robotics

This paper examines techniques for real-time terrain classification and solar irradiance mapping for outdoor, solar-powered mobile robots using a vision-based Artificial Neural Network (ANN). This process is completed sequentially. First, terrain classification is completed by extracting key features from visual-spectrum images captured from an on-board camera using Haar wavelet transform to identify both color and textural information. These features are then classified using an ANN to identify grass, concrete, asphalt, gravel, and mulch. Using the terrain classes, the image is then analyzed using concepts from high dynamic range imagery to establish the solar irradiance map of the area. In this way, our sequential methodology presented allows unmanned vehicles to classify the terrain and map the irradiance of a given area with no prior knowledge. Whereas, the terrain classification can be used in determining energy consumption or traversability criteria and the irradiance map can be used to estimate the energy harvesting capabilities.