Python Library for simulating unManNed vehiclEs(PLANE) is an open source software module, written in Python, that focuses on Unmanned Aerial Vehicles (UAVs), on their movements and on the mechanics of flight, thus devoting particular attention to the equations that describe drones' movement. In the context of the Internet of Drones (IoD), the module can be widely used for the study of the mutual control of position coordination in scenarios in which drones may find obstacles, as it happens in densely populated urban scenarios. Emphasis is put on ease of use, performance evaluation, documentation, and Application Programming Interface (API) consistency. The software tool has minimal dependencies and is distributed under MIT License. Source code, binaries, and documentation can be downloaded from GitHub.
@cite_16 represents a very interesting model-based test for the well-known Ardupilot platform http: ardupilot.org . Still, the contribution is not a simulator, as it builds a model that addresses core functionalities of autonomous flight in fail-safe conditions. The proposed control loop improves the existing solution leveraging efficient operations and solving some bugs. The proposal is of relevance since it contributes to the development of the platform.
@cite_16
2473252236 2611136914 2743408048 1907340149
Key to the operation of robot drones is the autopilot software that realizes the low-level control. The correctness of autopilot implementations is currently mainly verified based on simulations. These may overlook the timing aspects of control loop executions, which are however fundamental to dependable operation. We report on our experience in applying model-based real-time testing to Ardupilot, a widely adopted autopilot. We describe our approach at deriving a model of Ardupilot's core functionality and at reducing the model to enable practical testing. Our work reveals that Ardupilot may fail in meeting the time constraints associated to critical functionality, such as enabling fail-safe operation. Through controlled experiments, we demonstrate the real-world occurrence of such erroneous executions. The proven efficacy of learning-based control schemes strongly motivates their application to robotic systems operating in the physical world. However, guaranteeing correct operation during the learning process is currently an unresolved issue, which is of vital importance in safety-critical systems. We propose a general safety framework based on Hamilton-Jacobi reachability methods that can work in conjunction with an arbitrary learning algorithm. The method exploits approximate knowledge of the system dynamics to guarantee constraint satisfaction while minimally interfering with the learning process. We further introduce a Bayesian mechanism that refines the safety analysis as the system acquires new evidence, reducing initial conservativeness when appropriate while strengthening guarantees through real-time validation. The result is a least-restrictive, safety-preserving control law that intervenes only when (a) the computed safety guarantees require it, or (b) confidence in the computed guarantees decays in light of new observations. We prove theoretical safety guarantees combining probabilistic and worst-case analysis and demonstrate the proposed framework experimentally on a quadrotor vehicle. Even though safety analysis is based on a simple point-mass model, the quadrotor successfully arrives at a suitable controller by policy-gradient reinforcement learning without ever crashing, and safely retracts away from a strong external disturbance introduced during flight. This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot. This work presents the design of an open-source control architecture specifically tailored to the rapid development and testing of control and coordination algorithms on micro quadrotors. The proposed design extends an existing open-source and open-hardware quadrotor project, i.e., the Crazyflie nano quadrotor, by adding the guidance, navigation and control layers required to accomplish autonomous flight. The control layer, in particular, is based on a cascade control algorithm able to globally stabilize the position and the attitude of the vehicle. The global property guaranteed by the algorithm allows the quadrotors to perform aggressive maneuvers. The guidance layer is designed to coordinate simultaneously multiple vehicles by generating suitable reference trajectories. Experiments demonstrate the effectiveness of the proposed design.