Dottorato in INGEGNERIA INFORMATICA

Titolo della tesi: Aerial Swarm Robotics for Precision Agriculture

Aerial swarm robotics represents a highly scalable solution to autonomous systems with collaborative Unmanned Aerial Vehicles (UAVs) by means of decentralized decision making. The deployment of such system leads to numerous challenges and most of the developed research is limited to validation within simplified simulation environments. This produces solutions that cannot be readily applied to real world applications despite the promising features and demonstrated advantages of swarm robotics as shown in laboratory settings. In this thesis, our interest lies on the application of aerial swarm robotics in precision agriculture, with the purpose of using collaborative UAVs to optimize data gathering and resources utilisation in farms. Moreover, we focus on the active monitoring and mapping of crop fields implementing on board data analysis decision making. The overall goal is to deploy swarms of UAVs and achieve an overall error reduction of weed detection over time that is faster than baselines approaches based on pre-determined mission plans, where UAVs are exploited as passive sensors. In this thesis, we seek to bring a significant step into the transition from simulation to real world deployment. To this end, we develop simulation tools that provide photo-realistic rendering and high fidelity UAV dynamics. An experimental setup is presented for validation of all simulation components which are eventually combined into a comprehensive simulation environment. Deployment of real world hardware is considered to replicate realistic parameters to the developed simulators. We first focus on validation of a photo-realistic simulation based on the Unity video game engine to ensure adequate UAV perception of crop fields. The results are validated by comparing classification performance of Convolutional Neural Networks (CNNs) on real data after being trained separately on synthetic and real images. We considered the simulation of sugar beets, sunflowers and potatoes crops and simulation of Near Infrared Sensor (NIR) data for the sugar beets and sunflowers crops. The implemented CNNs trained with synthetic data achieve an Intersection Over Union (IoU) classification performance similar to the one obtained with real data. Secondly, we develop a simplified a simulation environment to validate a novel swarm strategy for monitoring and mapping of crop fields. This strategy is based on exploiting the available knowledge about the field to measure the Information Gain (IG) from visiting new areas, that is, the expected reduction of uncertainty from further observation of a given area. This results is a parameter-free control system achieving a significant improvement over the state of the art Reinforced Random Walk (RRW) approach. Finally, we implement an information-based monitoring and mapping strategy into a combined simulation with highly-realistic UAV perception and motion dynamics. The final simulation environment allowed us to simulate a swarm of UAVs within a field while doing online image classification and collaborative decision making. The previously developed strategy is adapted to fit into the new realistic conditions of the simulator and experimental setup. Furthermore, we seek to exploit the information available by observing areas of interest from multiple Point of view (POV). Thus, the UAVs prioritize positions where residual uncertainty of information of observation is maximal. This results in a better performance than a simplified random walk and the pre-planned optimal trajectory. Preliminary testing with real world UAVs is made to provide validation of the parameters established on the final simulation environment.