Servomotors confers structural stiffness to the drone

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servomotors confers structural stiffness to the drone as proven by the lack of deformations and oscillations of the arms during flight. However, the current design is not crash resilient. Collisions force the arms to fold producing a torque overload on the servomotors. This limitation can be overcome with the integration of lightweight dual-stiffness mechanisms [23], [24] to decouple the arms from the servomotors during collisions. Fig. 4: A close-up picture of our foldable drone reporting the main component used. (1) The Qualcomm Snapdragon Flight onboard computer, provided with a quad-core ARM processor, 2 GB of RAM, an IMU and two cameras. (2) The Qualcomm Snapdragon Flight ESCs. (3) The Arduino Nano microcontroller. (4) The servo motors used to fold the arms. All the computations necessary for autonomous flight are performed onboard. The state of the quadrotor (i.e., its po- sition, orientation, linear and angular velocities) is estimated using the Visual-Inertial Odometry pipeline provided by the Qualcomm mvSDK. Such state estimate is fed to the flight stack described in Sec. III, which runs onboard using ROS. B. Morphing Trade-Offs For each configuration presented in this work (X, T, H, O) we run in-flight experiments and performed offline evaluations in order to assess their respective advantages and trade-offs. More specifically, we are interested in: Flight time: the time the quadrotor can fly, which is affected by the arm configuration due to the overlap be- tween different propellers, as well as between propellers and the main body, and due to an asymmetric usage of the motors leading to over power consumption, for example in the T configuration; Maximum angular acceleration as controllability index: defined as the maximum angular acceleration the robot can produce in hover around the body x b - y b axes; Size: defined as the propeller tip-to-tip distance, for both the x b and the y b axes. Fig. 5 provides a comparison among the different morpholo- gies in terms of the aforementioned parameters, which are explained in the following. It is important to notice that the values reported in Fig. 5 are normalized by those obtained in the X configuration. In other words, for each parameter p i in a configuration i , Fig. 5 reports the ratio p i p X (or its inverse, as for the size), where p X is the same parameter evaluated in the X configuration. This is due to the fact that such a configuration is the most commonly used morphology for quadrotors, and, therefore, we took it as the reference model to evaluate advantages and disadvantages of the other configurations. Also, normalizing each value by the one obtained in the X configuration has the additional advantage of providing results
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6 IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED NOVEMBER, 2018 0.00 0.40 0.80 1.20 1.60 2.00 x-Size y-Size Flight Time Roll Acc.
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