Extending an Online (Re)Planning Platform for Crop Mapping with Autonomous UAVs through a Robotic Execution Framework

Abstract

Maps of plant pests are widely used to support farmer decisions to manage production at the scale of crop fields. Such maps are generally obtained manually, by human annotators or with human-controlled Unmanned Aerial Vehicles (UAVs), but this process is slow and costly. We propose an AI planning approach to fly fully autonomous UAVs equipped with on-line sequential decision-making capabilities for pests sampling and mapping in crop fields. We use a Markov Random Field framework to represent knowledge about the uncertain map and its quality, in order to compute an optimised pest-sampling policy. Since this planning problem is PSPACE hard, thus too complex to be solved exactly, we thus interleave planning and execution, generating plans from a subset of sampling sites selected. This approach has already been proved to be successful (Albore et al. 2015), favourably comparing with existing methods, but encounters some computation limits due to this division of tasks, considering that the planning execution framework is not adapted to the anytime-like behavior needed by real-world applications. We discuss the next steps in developing our approach, namely integrating the planning process and calculus of probabilities distribution in a framework able to deal with task management and execution under time constraints. Such extension, and integration within the AMPLE robotic execution framework, is promising as it associates the success of the replanning approach to the flexibility of an anytime executing architecture