Autonomous Navigation in Partially-Known Environment using Nano Drones with AI-based Obstacle Avoidance : A Vision-based Reactive Planning Approach for Autonomous Navigation of Nano Drones

Abstract: The adoption of small-size Unmanned Aerial Vehicles (UAVs) in the commercial and professional sectors is rapidly growing. The miniaturisation of sensors and processors, the advancements in connected edge intelligence and the exponential interest in Artificial Intelligence (AI) are boosting the affirmation of autonomous nano-size drones in the Internet of Things (IoT) ecosystem. However, achieving safe autonomous navigation and high-level tasks like exploration and surveillance with these tiny platforms is extremely challenging due to their limited resources. Lightweight and reliable solutions to this challenge are subject to ongoing research. This work focuses on enabling the autonomous flight of a pocket-size, 30-gram platform called Crazyflie in a partially known environment. We implement a modular pipeline for the safe navigation of the nano drone between waypoints. In particular, we propose an AI-aided, vision-based reactive planning method for obstacle avoidance. We deal with the constraints of the nano drone by splitting the navigation task into two parts: a deep learning-based object detector runs on external hardware while the planning algorithm is executed onboard. For designing the reactive approach, we take inspiration from existing sensorbased navigation solutions and obtain a novel method for obstacle avoidance that does not rely on distance information. In the study, we also analyse the communication aspect and the latencies involved in edge offloading. Moreover, we share insights into the finetuning of an SSD MobileNet V2 object detector on a custom dataset of low-resolution, grayscale images acquired with the drone. The results show the ability to command the drone at ∼ 8 FPS and a model performance reaching a COCO mAP of 60.8. Field experiments demonstrate the feasibility of the solution with the drone flying at a top speed of 1 m/s while steering away from an obstacle placed in an unknown position and reaching the target destination. Additionally, we study the impact of a parameter determining the strength of the avoidance action and its influence on total path length, traversal time and task completion. The outcome demonstrates the compatibility of the communication delay and the model performance with the requirements of the real-time navigation task and a successful obstacle avoidance rate reaching 100% in the best-case scenario. By exploiting the modularity of the proposed working pipeline, future work could target the improvement of the single parts and aim at a fully onboard implementation of the navigation task, pushing the boundaries of autonomous exploration with nano drones.