The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone’s conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimisation to a multi-body winged drone with trajectory optimisation to solve the motion intelligence problem under diverse flight mission requirements, such as energy consumption and mission completion time. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and mission time, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.

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. To facilitate the exploration of APG, we open-source our code and make it available atgithub.com/lis-epfl/apg_trajectory_tracking.

Fixed-wing drones must navigate to the desired location accurately for maneuvers such as picking up objects and perching. However, current GNSS receivers limit their navigation accuracy to several meters in outdoor environments, making such maneuvers impossible. RTK GNSS can improve flight accuracy, but it requires ground stations at the target location and additional communication modules on the drone. Here, we describe a fixed-wing platform with onboard computation that uses positional information from a GNSS receiver and vision from an onboard camera. The drone relies on a GNSS signal for flying towards a point of interest and switches to vision-based information to accurately reach the target. We conducted outdoor experiments to compare the flight accuracy of three navigation methods: GNSS, RTK GNSS, and the proposed GNSS-vision method. We also systematically assessed the robustness of vision-based control to compensate for GNSS errors and quantify the accuracy of the proposed method. Our results show that the accuracy of the proposed GNSS-vision system is on par with RTK GNSS. GNSS-vision reduces the average error of GNSS by over an order of magnitude, from 3.033 m to 0.283 m, and reduces the variance across repeated flights from 2.095 m to 0.309 m. We open-source the software-hardware architecture used in this paper to enable the research community to build on these results and expand the capabilities of fixed-wing drones.

Accurate knowledge of geometric and inertia parameters are a necessity for precise and robust control of aerial vehicles. We propose a novel filter that is able to fuse motor speed, inertia, and pose measurements to estimate the vehicle's key dynamic properties online. The presented framework is able to estimate the multirotor's moment of inertia, mass, center of mass and each sensor module's relative position. Obtaining these estimates in-flight allow the multirotor to be precisely controlled even during tasks such as load transportation or after configuration changes on scene. We provide a nonlinear observability analysis, proving that the presented model is locally weakly observable. Experimental results validate the proposed approach, showing the ability to estimate the dynamic properties accurately and demonstrate its capability to do so even while additional loads are added. The framework is flexible and can easily be adapted to a wide range of applications, including self-calibration, object grasping, and single robot or multi-robot payload transportation.