Overactuated omnidirectional flying vehicles are capable of generating force and torque in any direction, which is important for applications such as contact-based industrial inspection. This comes at the price of an increase in model complexity. These vehicles usually have non-negligible, repetitive dynamics that are hard to model, such as the aerodynamic interference between the propellers. This makes it difficult for high-performance trajectory tracking using a model-based controller. This paper presents an approach that combines a datadriven and a first-principle model for the system actuation and uses it to improve the controller. In a first step, the first-principle model errors are learned offline using a Gaussian Process (GP) regressor. At runtime, the first-principle model and the GP regressor are used jointly to obtain control commands. This is formulated as an optimization problem, which avoids ambiguous solutions present in a standard inverse model in overactuated systems, by only using forward models. The approach is validated using a tilt-arm overactuated omnidirectional flying vehicle performing attitude trajectory tracking. The results show that with our proposed method, the attitude trajectory error is reduced by 32% on average as compared to a nominal PID controller.

This paper presents a novel type of flying vehicle called the Monospinner, which has only one moving part, the propeller, and is yet able to hover and fully control its position. Its translational and attitude dynamics are formulated as a twelve-dimensional state space system, which may be linearized to a linear time-invariant system amenable to controllability analysis, controller synthesis, and vehicle design. It is shown that the linearized system may be both horizontally and vertically controllable in position after removing its yaw state, and in particular, this is shown for the case of a vehicle with the shape of a planar object and an offset thrust location (with respect to its center of mass). The vehicle's mass distribution is designed based on two robustness metrics: the ability to maintain hover under perturbations by means of Monte-Carlo nonlinear simulation, and the probability of input saturation based on a stochastic model. Experiments are conducted for the resulting vehicle and controller. The equilibrium of the resulting system has a large region of attraction such that it recovers after being thrown into the air like a frisbee.

The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including unknown spatially-varying friction properties and discontinuous variations of the surface geometry. Finding a control strategy that is robust against these environmental uncertainties remains an open challenge. This paper presents a learning-based adaptive control strategy for aerial sliding tasks. In particular, the gains of a standard impedance controller are adjusted in real-time by a policy based on the current control signals, proprioceptive measurements, and tactile sensing. This policy is trained in simulation with simplified actuator dynamics in a student-teacher learning setup. The real-world performance of the proposed approach is verified using a tiltarm omnidirectional flying vehicle. The proposed controller structure combines data-driven and model-based control methods, enabling our approach to successfully transfer directly and without adaptation from simulation to the real platform. Compared to fine-tuned state of the art interaction control methods we achieve reduced tracking error and improved disturbance rejection.