Background Current near-infrared spectroscopy (NIRS)-based systems for continuous flap monitoring are highly sensitive for detecting malperfusion. However, the clinical utility and user experience are limited by the wired connection between the sensor and bedside console. This wire leads to instability of the flap-sensor interface and may cause false alarms. Methods We present a novel wearable wireless NIRS sensor for continuous fasciocutaneous free flap monitoring. This waterproof silicone-encapsulated Bluetooth-enabled device contains two light-emitting diodes and two photodetectors in addition to a battery sufficient for 5 days of uninterrupted function. This novel device was compared with a ViOptix T.Ox monitor in a porcine rectus abdominus myocutaneous flap model of arterial and venous occlusions. Results Devices were tested in four flaps using three animals. Both devices produced very similar tissue oxygen saturation (StO 2) tracings throughout the vascular clamping events, with obvious and parallel changes occurring on arterial clamping, arterial release, venous clamping, and venous release. Small interdevice variations in absolute StO 2 value readings and magnitude of change were observed. The normalized cross-correlation at zero lag describing correspondence between the novel NIRS and T.Ox devices was >0.99 in each trial. Conclusion The wireless NIRS flap monitor is capable of detecting StO 2 changes resultant from arterial vascular occlusive events. In this porcine flap model, the functionality of this novel sensor closely mirrored that of the T.Ox wired platform. This device is waterproof, highly adhesive, skin conforming, and has sufficient battery life to function for 5 days. Clinical testing is necessary to determine if this wireless functionality translates into fewer false-positive alarms and a better user experience.

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Optimal control holds great potential to improve a variety of robotic applications. The application of optimal control on-board limited platforms has been severely hindered by the large computational requirements of current state of the art implementations. In this work, we make use of a deep neural network to directly map the robot states to control actions. The network is trained offline to imitate the optimal control computed by a time consuming direct nonlinear method. A mixture of time optimality and power optimality is considered with a continuation parameter used to select the predominance of each objective. We apply our networks (termed GCNets) to aggressive quadrotor control, first in simulation and then in the real world. We give insight into the factors that influence the 'reality gap' between the quadrotor model used by the offline optimal control method and the real quadrotor. Furthermore, we explain how we set up the model and the control structure on-board of the real quadrotor to successfully close this gap and perform time-optimal maneuvers in the real world. Finally, GCNet's performance is compared to state-of-the-art differential-flatness-based optimal control methods. We show, in the experiments, that GCNets lead to significantly faster trajectory execution due to, in part, the less restrictive nature of the allowed state-to-input mappings.

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Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping and visual inertial odometry, which are computationally expensive for micro aerial vehicles (MAVs), we developed the highly efficient snake gate detection algorithm for visual navigation, which can detect the gate at 20 HZ on a Parrot Bebop drone. Then, with the gate detection result, we developed a robust pose estimation algorithm which has better tolerance to detection noise than a state-of-the-art perspective-n-point method. During the race, sometimes the gates are not in the drone's field of view. For this case, a state prediction-based feed-forward control strategy is developed to steer the drone to fly to the next gate. Experiments show that the drone can fly a half-circle with 1.5 m radius within 2 s with only 30cm error at the end of the circle without any position feedback. Finally, the whole system is tested in a complex environment (a showroom in the faculty of Aerospace Engineering, TU Delft). The result shows that the drone can complete the track of 15 gates with a speed of 1.5m∕s which is faster than the speeds exhibited at the 2016 and 2017 IROS autonomous drone races.

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Drones, especially quadrotors, have shown their great value for applications like aerial photography, object delivery and warehouse inspection. At the same time, with the de- velopment of Artificial Intelligence (AI), computers can replace humans and even per- form better than humans in some areas where it was impossible before like the AI pro- gram Alpha Go which beat the human world champion in Go matches and Alpha star which was rated above 99.8% human players in the real-time strategy game StarCraft II. Concerning drones, the question is whether they can fly races completely by themselves and if they can fly even faster than human pilots’ racing drones? Although there exist many technologies for drones to fly autonomously in terms of navigation, guidance and control, autonomous drone racing still sets an enormous chal- lenge for the robotics community. For example, the most commonly used vision camera based navigation technologies such as Simultaneous Localization and Mapping (SLAM) and Visual Inertial Odometry (VIO) suffer motion blur when the drone moves fast and high computational demand which is scarce onboard the drone. Moreover, the com- monly used PID controller has no guarantees of optimality while much parameter tuning is required. Many other challenges like these require new technologies to satisfy more complex and challenging flying scenarios to challenge humans in drone races. This thesis attempts to answer the question mentioned above. First of all, this the- sis presents 2 systematic solutions for autonomous drone racing including navigation, guidance and control techniques. The solutions are computationally so efficient that they can run on board of a Bebop 1 quadrotor (made in 2014) without using the GPU and a cheap 72-gram quadrotor called the ’Trashcan’. With the constraints of the processing power and cheap onboard sensors, the Bebop can fly through 15 gates in a complex sce- nario with an average speed of 1.5m/s and the Trashcan can fly through a 4-gate racing track for 3 laps with an average speed of 2m/s. Both solutions helped the MAVLab, TU Delft, participate in the IROS autonomous drone racing in 2017 and 2018. In terms of visual navigation, a computationally efficient gate detection method ’snake gate’ is developed to detect the racing gate during the flight. Together with a revised version of Perspective-3-Point (P3P) method, the detection results are used to provide location information for the drone. A Kalman filter is developed to fuse these detec- tions with the onboard IMU readings. Unlike the traditional Kalman filter, this version deduces the velocity from the accelerometers readings by a linear drag model approx- imation instead of integrating the accelerometers. In this way, the Kalman filter has a faster convergence rate. Another filtering method, Visual Model-predictive Localization (VML), is also developed to fuse the vision detections and onboard attitude estimation. The simulation and real-world flight results show that the VML is more robust to outliers than the commonly used Kalman filter especially when there are invalid measurements. Also, the VML is more efficient than the Kalman filter in handling measurement delays. At last, a gradient descent based parameter estimation method is developed to estimate the quadrotor’s aerodynamics coefficients and the Attitude and heading reference sys- tem (AHRS) biases using the visual measurements and the onboard state predictions. With the estimated parameters, the quadrotor can have a better state prediction when no visual measurement is available in some time. In terms of guidance and control, a novel neural network based nonlinear optimal controller, G&CNet, is developed to steer the drone to the target with the minimum time. This G&CNet moves the time-consuming nonlinear controller onboard and can be run at 200HZ to map the current states and the optimal control policy calculated offboard. The simulation results show that the flying result is very close to the theoretical nonlinear optimal control solution. Both simulation and real-world flying results show that it has faster flights than a commonly used polynomial based trajectory generation and tracking method. Last but not least, the methods provided can be generalized to other applications. For example, for the outdoor flight where the Global Positioning System (GPS) is avail- able for navigation, the vision measurements can be directly replaced by the GPS signals in the proposed navigation strategies and they should work directly. For the proposed G&CNet, it should work in all scenarios where the guidance and control modules are needed to move the drone from one point to another point. In this way, the proposed methods allow drones to move faster in a robust way, extending their mission capabili- ties. @en

Drone racing is becoming a popular e-sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a novel sensor fusion method called visual model-predictive localization (VML). Within a small time window, VML approximates the error between the model prediction position and the visual measurements as a linear function. Once the parameters of the function are estimated by the RANSAC algorithm, this error model can be used to compensate the prediction in the future. In this way, outliers can be handled efficiently and the vision delay can also be compensated efficiently. Theoretical analysis and simulation results show the clear advantage compared with Kalman filtering when dealing with the occasional large outliers and vision delays that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named “Trashcan,” which was equipped with a Jevois smart camera for a total of 72 g. An average speed of 2 m/s is achieved while the maximum speed is 2.6 m/s. To the best of our knowledge, this flying platform is currently the smallest autonomous racing drone in the world, while still being one of the fastest autonomous racing drones.

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High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of micro air vehicles. Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. These properties hamper the use of standard visual odometry approaches and imply that the micro air vehicles will have to bridge considerable time intervals without position feedback. To this end, we propose an approach to trajectory estimation for drone racing that is computationally efficient and yet able to accurately estimate a micro air vehicle’s state (including biases) and parameters based on sparse, noisy observations of racing gates. The key concept of the approach is to optimize unknown and difficult-to-observe state variables so that the observations of the racing gates best fit with the known control inputs, estimated attitudes, and the quadrotor dynamics and aerodynamics during a time window. It is shown that a gradient-descent implementation of the proposed approach converges ∼4 times quicker to (approximately) correct bias values than a state-of-the-art 15-state extended Kalman filter. Moreover, it reaches a higher accuracy, as the predicted end-point of an open-loop turn is on average only ∼20 cm away from the actual end-point, while the extended Kalman filter and the gradient descent method with kinematic model only reach an accuracy of ∼50 cm. Although the approach is applied here to drone racing, it generalizes to other settings in which a micro air vehicle may only have sparse access to velocity and/or position measurements.

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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.@en