Mid-air, near mid-air, and on-ground collisions are among the main causes of accident in general aviation. The aim of ODESSA (Obstruction DEtection Sensor for Surveillance on Aircraft) project is to provide both fixed and rotary wing aircraft, piloted on board or remotely, with a small, light, and affordable obstacle detection device. This to prevent collisions while manoeuvring close to the terrain (i.e. take-off and landing phases) or on-ground (i.e. taxiing phase). This objective has been achieving by exploiting the experience gained in the automotive market, where low-cost but trustworthy radars, sometimes combined with video cameras, provide vehicles with reliable collision detection capabilities. Named sensors are based on millimetre waves radar principles. Radar provides target detection and camera, if present, improves detection reliability (ghost echo removal). This paper investigates and demonstrates the technical feasibility of adapting an automotive radar sensor for avionic application needs by integrating a Frequency Modulated Continuous Wave (FMCW) 76.5 GHz radar sensor on a Remoted Piloted Aircraft System (RPAS) for obstacle detection purposes. The combined application of Range-Doppler FFT, CFAR techniques for targets detection, and Direction-of-Arrival (DoA) estimation methods such as Range-Angle FFT, Barlett beamformer, MVDR beamformer and MUSIC/SODIN-MUSIC algorithms will be discussed in terms of results and performance. A first prototype, composed of radar sensor and camera, has been realized and tested on the field. The prototype has been rolled out in two international events as the 53rd Salon Internationale de l’Aéronautique et de l’Espace and the Maker Fair Rome 2019. The development of the system in the near future will include the integration of artificial intelligence techniques applied to computer vision, for the identification and classification of obstacles. This will remarkably enhance the collision avoidance capabilities.