Autonomous Real-Time Site Selection for Venus and Titan Landing using Evolutionary Fuzzy Cognitive Maps

Future science-driven landing missions, conceived to collect in-situ data on regions of planetary bodies that have the highest potential to yield important scientific discoveries, will require a higher degree of autonomy. The latter includes the ability of the spacecraft to autonomously select the landing site using real-time data acquired during the descent phase. This paper presents the development of an Evolutionary Fuzzy Cognitive Map (E-FCM) model that implements an artificial intelligence system capable of selecting a landing site with the highest potential for scientific discoveries constrained by the requirement of soft landing on a region with safe terrain. The proposed E-FCM evolves its internal states and interconnections as function of the external data collected during the descent, therefore improving the decision process as more accurate information is available. The E-FCM is constructed using knowledge accumulated by experts and it is tested on scenarios that simulate the decision-making process during the descent toward the Hyndla Regio on Venus. The EFCM is shown to quickly reach conclusions that are consistent with what a planetary expert would decide if the scientist were presented, in real-time, with the same available information. The proposed methodology is fast and efficient and may be suitable for on-board spacecraft implementation and real-time decision-making during the course of any robotic exploration of the Solar System.