Poster Abstract: Terrain Navigability Assessment of Autonomous Ground Robots Using mmWave Radar

We present a parameter evaluation of FMCW mmWave Radar to assess surface dampness and ruggedness and enhance the navigability of autonomous ground robots. We begin by designing and 3D-printing a mount for the mmWave Radar on a Rosmaster X3 platform. We then collect raw mmWave Radar data from various surfaces (grass, soil, puddles, and mulch) across different seasons (summer, winter, and rainy). Our findings demonstrate that the energy strength parameter is a reliable indicator for assessing the surface: dry surfaces (e.g., dry grass, dry mud) exhibit lower energy strength, whereas wet surfaces display higher values. This dampness and ruggedness factor can be leveraged to develop a cost-map navigability score for autonomous ground robots.