Flight in dense environments, such as forests and cities requires drones to perform sharp turns. Although fixed-wing drones are aerodynamically and energetically more efficient than multicopters, they require a comparatively larger area to turn and thus are not suitable for fast flight in confined spaces. To improve the turning performance of winged drones, here we propose to adopt an avian-inspired strategy of wing folding and pitching combined with a folding and deflecting tail. We experiment in wind tunnel and flight tests how such morphing capabilities increase the roll rate and decrease the turn radius - two measures used for assessing turn performance. Our results indicate that asymmetric wing pitching outperforms asymmetric folding when rolling during cruise flight. Furthermore, the ability to symmetrically morph the wing and tail increases the lift force, which notably decreases the turn radius. These findings pave the way for a new generation of drones that use bird-like morphing strategies combined with a conventional propeller-driven thrust to enable aerodynamic efficient and agile flight in open and confined spaces.
Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but require high computing power. Conversely, learning-based offline optimization approaches, such as Reinforcement Learning (RL), allow fast and efficient execution on the robot but hardly match the accuracy of MPC in trajectory tracking tasks. In systems with limited compute, such as aerial vehicles, an accurate controller that is efficient at execution time is imperative. We propose an Analytic Policy Gradient (APG) method to tackle this problem. APG exploits the availability of differentiable simulators by training a controller offline with gradient descent on the tracking error. We address training instabilities that frequently occur with APG through curriculum learning and experiment on a widely used controls benchmark, the CartPole, and two common aerial robots, a quadrotor and a fixed-wing drone. Our proposed method outperforms both model-based and model-free RL methods in terms of tracking error. Concurrently, it achieves similar performance to MPC while requiring more than an order of magnitude less computation time. Our work provides insights into the potential of APG as a promising control method for robotics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.