Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform- and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.

Deep Drone Racing: From Simulation to Reality with Domain Randomization

The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. In particular, deep learning techniques for motion control have recently taken a major qualitative step, since the successful application of Deep Q-Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide outstanding results with continuous state and action domains, which are a requirement in most of the robotics-related tasks. In this context, the research community is lacking the integration of realistic simulation systems with the reinforcement learning paradigm, enabling the application of deep reinforcement learning algorithms to the robotics field. In this paper, a versatile Gazebo-based reinforcement learning framework has been designed and validated with a continuous UAV landing task. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm, which has been integrated in our reinforcement learning framework. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. As an indirect result, a powerful work flow for robotics has been validated, where robots can learn in simulation and perform properly in real operation environments. To the best of the authors knowledge, this is the first work that addresses the continuous UAV landing maneuver on a moving platform by means of a state-of-the-art deep reinforcement learning algorithm, trained in simulation and tested in real flights.

/pdf/a-deep-reinforcement-learning-strategy-for-uav-autonomous-1sbtcw17g7.pdf

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.

/pdf/a-64mw-dnn-based-visual-navigation-engine-for-autonomous-rwj286yjqm.pdf

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

Robust localization is critical for the navigation and control of mobile robots Global Navigation Satellite Systems (GNSS), Visual-Inertial Odometry (VIO), and Simultaneous Localization and Mapping (SLAM) offer different methods for achieving this goal In some cases however, these methods may not be available or provide high enough accuracy In such cases, these methods may be augmented or replaced with fiducial marker pose estimation Fiducial markers can increase the accuracy and robustness of a localization system by providing an easily recognizable feature with embedded fault detection This paper presents an overview of fiducial markers developed in the recent years and an experimental comparison of the four markers (ARTag, AprilTag, ArUco, and STag) that represent the state-of-the-art and most widely used packages These markers are evaluated on their accuracy, detection rate and computational cost in several scenarios that include simulated noise from shadows and motion blur Different marker configurations, including single markers, planar and non-planar bundles and multi-sized marker bundles are also considered in this work

Fiducial Markers for Pose Estimation

We present a quadrotor system capable of autonomously landing on a moving platform using only onboard sensing and computing. We rely on state-of-the-art computer vision algorithms, multi-sensor fusion for localization of the robot, detection and motion estimation of the moving platform, and path planning for fully autonomous navigation. Our system does not require any external infrastructure, such as motion- capture systems. No prior information about the location of the moving landing target is needed. We validate our system in both synthetic and real-world experiments using low-cost and lightweight consumer hardware. To the best of our knowledge, this is the first demonstration of a fully autonomous quadrotor system capable of landing on a moving target, using only on-board sensing and computing, without relying on any external infrastructure.

/pdf/vision-based-autonomous-quadrotor-landing-on-a-moving-4a6fjpz3v8.pdf

Vision-based autonomous quadrotor landing on a moving platform

Autonomous drone racing (ADR) is a challenge for autonomous drones to navigate a cluttered indoor environment without relying on any external sensing in which all the sensing and computing must be done with onboard resources. Although no team could complete the whole racing track so far, most successful teams implemented waypoint tracking methods and robust visual recognition of the gates of distinct colors because the complete environmental information was given to participants before the events. In this paper, we introduce the purpose of ADR as a benchmark testing ground for autonomous drone technologies and analyze challenges and technologies used in the two previous ADRs held in IROS 2016 and IROS 2017. Five teams which participated in these events present their implemented technologies that cover modified ORB-SLAM, robust alignment method for waypoints deployment, sensor fusion for motion estimation, deep learning for gate detection and motion control, and stereo-vision for gate detection.

Challenges and implemented technologies used in autonomous drone racing

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Vision-based autonomous quadrotor landing on a moving platform

Challenges and implemented technologies used in autonomous drone racing