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

Studies of past life forms in 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 as desiccation cracks are normally located in complex environments and are difficult to identify with the naked eye. However, as most UAVs have limited onboard decision-making capabilities for autonomous navigation in such environments, human operators have a strong reliance on their communication systems to operate them. This paper proposes a UAV-based 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 using the Augmented Belief Tree (ABT) online solver. The UAV system is tested with Hardware in the Loop (HIL) simulations and real flight tests using two desiccation crack patterns distributed across an open area. Real-time segmentation from streamed camera frames of desiccation cracks is achieved through inference with a custom Convolution Neural Network (CNN) model that uses an architecture based on ResNet18, and an OpenCV AI Kit(OAK)-D camera. Real test results show that the system is capable of detecting and mapping the surveyed area with desiccation cracks effectively. 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 and surveillance.