Autonomous Mapping of Desiccation Cracks via a Probabilistic-based Motion Planner Onboard UAVs

Studies of past life forms on other planets are possible through the identification and localisation of desiccation cracks in ancient water bodies such as lakes, rivers and seas. Unmanned aerial vehicles (UAVs) are increasingly being used as a viable remote sensing solution for planetary exploration, as desiccation cracks are difficult to identify with the naked eye and are normally located in complex and unreachable environments. However, most UAVs have a strong reliance on human operators through their communication systems, as UAVs have limited onboard decision-making capabilities for autonomous navigation in such environments. UAV navigation in real-world scenarios is also challenging as data captured from their sensors is imperfect, and outputs from computer vision systems are, sometimes, inaccurate. These sensory and onboard vision limitations cause partial observability of the state of surveyed environments, inducing uncertainty in optimal path planning. This paper proposes a UAV system for autonomous onboard navigation, identification, and mapping of desiccation cracks for planetary exploration. The navigation problem is mathematically formulated as a partially observable Markov decision process (POMDP), where a motion strategy can be obtained by solving the POMDP in real time using the augmented belief tree (ABT) solver. The framework discussed in this work is validated with real flight tests using two desiccation crack patterns distributed across the surveyed area. Real-time segmentation from streamed camera frames of desiccation cracks is achieved through inference onboard the aircraft using a ResNet18 Convolutional Neural Network (CNN) model, and an OpenCV AI Kit (OAK)-D camera. Results from real flight tests indicate that the system can reduce levels of object detection uncertainty to locate and map desiccation cracks in environments under partial observability. The system design allows further adaptation for similar time-critical applications requiring increased levels of UAV autonomy in unstructured environments under uncertainty and partial observability, such as humanitarian relief, wildlife monitoring, and surveillance.