Showing papers by "Murat Bronz published in 2022"
TL;DR: In this paper , the authors estimate the average wind influencing a quadrotor drone only based on standard navigation sensors and equations of motion, which can be used in several situation, including atmospheric studies, trajectory planning under environmental constraints, or as a reference for studying flights in shear layer.
Abstract: The aim of this work is to estimate the average wind influencing a quadrotor drone only based on standard navigation sensors and equations of motion. It can be used in several situation, including atmospheric studies, trajectory planning under environmental constraints, or as a reference for studying flights in shear layer. For this purpose, a small quadrotor drone with spherical shape has been developed. Flight data are recorded from telemetry during indoor and outdoor flight tests and are post-processed. The proposed solution is based on a calibration procedure with global optimization to extract the drag model and a Kalman Filter for online estimation of the wind speed and direction. Finally, an on-board implementation of the real-time estimation is demonstrated with real flights in controlled indoor environment.
2 citations
20 Jun 2022
TL;DR: In this paper , the robustness of panel method based path planning algorithm under wind disturbances is evaluated experimentally and the results suggest that panel method-based path planning scheme maintains its obstacle avoidance property under wind disturbance.
Abstract: In this study, robustness of panel method based path planning algorithm under wind disturbances is evaluated experimentally. Panel method, borrowed from fluid dynamics domain, is a numeric tool for calculating the potential field around arbitrarily shaped objects. Resultant potential field can be used for generating collision free trajectories for uncrewed aerial vehicles with convergence guarantee. Robustness of the proposed method is demonstrated during indoor experiments with a wind generator creating wind speed up to 7 m/s and also during outdoor experiments with wind speeds ranging between 3 - 5 m/s. Experiment results suggest that panel method based path planning scheme maintains its obstacle avoidance property under wind disturbances.
2 citations
TL;DR: The main objectives were to fly into trade wind cumulus clouds to understand the microphysical processes involved in their evolution, as well as to provide a proof of concept of sensor-based adaptive navigation patterns to optimize the data collection.
Abstract: Drones are commonly used for civil applications and are accessible to those with limited piloting skills in several scenarios. However, the deployment of a fleet in the context of scientific research can lead to complex situations that require an important preparation in terms of logistics, permission to fly from authorities, and coordination during the flights. This paper is a field report of the flight campaign held at the Barbados Island as part of the NEPHELAE project. The main objectives were to fly into trade wind cumulus clouds to understand the microphysical processes involved in their evolution, as well as to provide a proof of concept of sensor-based adaptive navigation patterns to optimize the data collection. After introducing the flight strategy and context of operation, the main challenges and the solutions to address them will be presented, to conclude with the evaluation of some technical evolution developed from these experiments.
1 citations
10 May 2022
TL;DR: Wang et al. as mentioned in this paper proposed a panel method based path planning for electric vertical take off and landing vehicles in urban environments, which is tested in a high fidelity simulation environment and with real-life drones in an indoor flight arena.
Abstract:
In this study, previously proposed panel method based path planning for electric vertical take off and landing vehicles in urban environments is tested in a high fidelity simulation environment and with real-life drones in an indoor flight arena. Panel method is a numerical tool, borrowed form fluid dynamics domain, that can generate collision free paths for multiple vehicles in environments with arbitrarily shaped obstacles while guaranteeing obstacle avoidance and convergence to global minima with little computational load. In this study, panel method based path planning is further improved with introduction of novel safety source element that enables a safety perimeter around obstacles without losing convergence guarantee. Furthermore, path planning capability of panel method for electric vertical take off and landing vehicles in urban environments is demonstrated with hardware experiments in a scaled urban environment. Experiment results indicate that panel method is a promising tool for path planning in urban environments.
21 Jun 2022
TL;DR: In this article , a hybrid polynomial formulation for the modeling of the aerodynamic coefficients of a fixed-wing UAV at high angles of attack is presented, and a Linearly Constrained Least Squares (LCLS) process guaranteeing continuity at the mode transitions is proposed for identification of the model.
Abstract: Modeling the longitudinal dynamics of a fixed-wing unmanned aerial vehicle (UAV) at high angles of attack is not an easy task. Indeed, when the airplane approaches stall, non-linear effects appear, including transient behaviors and an aerodynamic hysteresis. Although some models are present in the literature to address these aspects, they are usually aerodynamics-based and often too complex for analysis and control applications. Therefore, this paper presents a new hybrid polynomial formulation for the modeling of the aerodynamic coefficients. In addition, a Linearly Constrained Least Squares (LCLS) process guaranteeing continuity at the mode transitions is proposed for the identification of the model. The Hybrid Polynomial Stall Model (HPSM) is finally identified on experimental wind tunnel data, showcasing its ability to accurately predict a UAV’s dynamics.
TL;DR: In this article , the authors use deep reinforcement learning to learn a closed-loop multi-agent real-time guidance strategy for quadrotors to autonomously perform inspections of runways.
Abstract: Aircraft runways are periodically inspected for debris and damage. Instead of having pilots coordinate the motion of the quadrotors manually or hand-crafting the desired quadrotor behavior into a guidance law, this paper reports the use of deep reinforcement learning to learn a closed-loop multiagent real-time guidance strategy for quadrotors to autonomously perform such inspections. This yields a significant reduction in engineering effort while enabling highly-flexible real-time performance. The runway is discretized into a number of rectangular tiles, which must all be visited for the runway to be considered inspected. The guidance system reported here calculates a desired acceleration in real time for the quadrotor(s) to track in order to complete the task. This paper first develops the guidance technique, trains it in simulation, and evaluates it experimentally using an indoor quadrotor laboratory. This process is then repeated for an outdoor setting on a real runway, where the proposed guidance strategy is compared to a handcrafted strategy and applied to a multiquadrotor scenario where the quadrotors must learn to coordinate their behavior and be resilient to the failure of one quadrotor mid-experiment. Multiagent, fault-tolerant, learned behavior is successfully demonstrated through outdoor quadrotor flights. Additional simulations and experiments demonstrate the technique is viable in a swarm with additional quadrotors, on a variety of runway shapes and with increased discretization of the runway. This work shows how modern learning-based techniques can: 1) reduce the engineering effort required to design complex guidance systems and 2) be implemented on real hardware in a representative outdoor environment.