Modern unmanned aerial vehicles (UAVs) with sophisticated mechanics ask for extended online system identification to aid model-based controls in task execution. In addition, UAVs in adverse environmental conditions require a more detailed environmental disturbance understanding. The necessary combination of online system identification, sensor suite self-calibration, and external disturbance analysis to tackle these issues holistically is currently an open issue. Our proposed FUSE-D approach combines these elements based on a system model at the rotor-speed level and a single global pose sensor (e.g., a tracking system like Optitrack). Besides sensor intrinsics and extrinsics, the framework allows estimating the UAV's rotor geometry, mass, moments of inertia, and the rotors' aerodynamic properties, as well as an external force and where it acts on the UAV. The general formulation allows us to extend the approach to an N-rotor (multi-rotor) UAV and classify the type of external disturbance. We perform a detailed non-linear observability analysis for the 43 + 7N states and do a statistically relevant embedded hardware-in-the-loop performance analysis in the realistic simulation environment Gazebo with RotorS.

In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly op-posite optimization objectives can be used to improve trajectory control tracking and, at the same time, estimation performance. Our proposed novel Control & Observability-aware Planning (COP) framework is the first that uses these possibly opposing objectives in a Single-Objective Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method to perform the balancing of them and generation of Bézier curve-based trajectories. Statistically relevant simulations for a 3D quadrotor unmanned aerial vehicle (UAV) case study produce results that support our claims and show the negative correlation between both objectives. We were able to reduce the positional mean integral error norm as well as the estimation uncertainty with the same trajectory to comparable levels of the trajectories optimized with individual objectives.

This article presents an entirely data-driven approach for autonomous control of redundant manipulators with hydraulic actuation. The approach only requires minimal system information, which is inherited from a simulation model. The non-linear hydraulic actuation dynamics are modeled using actuator networks from the data gathered during the manual operation of the manipulator to effectively emulate the real system in a simulation environment. A neural network control policy for autonomous control, based on end-effector (EE) position tracking is then learned using Reinforcement Learning (RL) with Ornstein-Uhlenbeck process noise (OUNoise) for efficient exploration. The RL agent also receives feedback based on supervised learning of the forward kinematics which facilitates selecting the best suitable action from exploration. The control policy directly provides the joint variables as outputs based on provided target EE position while taking into account the system dynamics. The joint variables are then mapped to the hydraulic valve commands, which are then fed to the system without further modifications. The proposed approach is implemented on a scaled hydraulic forwarder crane with three revolute and one prismatic joint to track the desired position of the EE in 3-Dimensional (3D) space. With the emulated dynamics and extensive learning in simulation, the results demonstrate the feasibility of deploying the learned controller directly on the real system.

Precise system identification is an important aspect of adequate control design and parameter definition to allow for accurate and reliable navigation. While this is well known in robotics, the community working with small rotorcraft Unmanned Aerial Vehicles (UAVs) has yet to discover the benefits. In contrast to existing work, which often performs offline or deterministic (i.e. closed-form) system identification, we present a probabilistic approach to the online estimation of system identification parameters and self-calibration states. Instead of decoupling system identification and state estimation for vehicle control, we merge the entire process into a holistic probabilistic framework to allow self-awareness and self-healing. Our observability analysis shows that most of the system identification parameters are observable and converge quickly to the optimal value using a combination of inertial cues, dynamic modeling, and an additional exteroceptive sensor. We support our theoretical findings with extensive tests simulating realistic data in Gazebo.