Unmanned aerial manipulators (UAMs) extend the operational reach of unmanned aerial vehicles (UAVs), yet are often limited by the underactuation of traditional UAV platforms, restricting the workspace of their end-effectors. This article introduces an overactuated UAM system comprising a biaxial-tilting thrust-vectoring quadrotor coupled with a serial manipulator. This configuration offers superior controllable performance, an expanded manipulator workspace, and robust disturbance rejection compared to conventional underactuated UAMs. To mitigate practical uncertainties and disturbances–including model imprecision, environmental factors, changing loads, and the coupling wrenches between the UAV and manipulator–a fixed-time (FxT) controller and a FxT disturbance observer are developed, leveraging a high-order fully actuated (HOFA) control strategy. The implicit Lyapunov function (ILF) approach is employed for stability analysis, offering a streamlined proof of convergence. Simulations and flight tests affirm the system's trajectory tracking precision, with positional and attitudinal accuracies within $1\;\text{cm}$ and $1^{\circ }$ , respectively.

The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP's lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims.

The robust multimodel solution for multiple-input single-output(MISO) nonlinear time-delay systems identification with polluted outputs is derived in this paper. First, all the local Autoregressive Exogenous(ARX) models are pre-identified at the working points; then the global system model is built by interpolating the local models with a smoothing strategy. The outliers and input time-delays which often increase the nonideality of process data, are both considered. To cope with the outliers, the Laplace distribution is reutilized to describe the output measurement process and the negative impact resulted from each outlier imposed on parameter estimation can be suppressed through automatically assigned a small weight. The parameter estimation procedure is realized with the expectation-maximization(EM) algorithm and the joint posterior probability of all input delays is also maximized to calculate the unknown input time-delays. With the verifications on a numerical example and the continuous fermentation process, the validity of the proposed approach is proved.

The aim of this article is the design of a feedforward compensator based on deep reinforcement learning (DRL) for cooperative quadrotors in close crossing flight. Quadrotors are described by state-space models that include shearing airflow disturbance from other quadrotors. This disturbance is compensated in a feedforward way using DRL. Both value based compensator and policy based compensator algorithms are proposed for training purposes. Then, Lyapunov stability criteria are used to prove that the reference trajectory can be tracked boundedly even during the training process of the proposed algorithms, and that a smaller bound of tracking error can be achieved when the compensator converges. An indoor experimental system for online training has been developed for validation purposes. Both simulation and experimental results are provided to demonstrate the effectiveness and advantages of the proposed compensator.

Multirotors have been extensively used due to their simple structure and flexible flight capabilities. However, traditional coplanar multirotors are underactuated in such a way that their 3-degree-of-freedom (DoF) position and 3-DoF attitude cannot be controlled simultaneously. This article proposes a new overactuated flight platform using biaxial-tilt actuation unit (BTAU), called Quad3DV , with capability of antidisturbance 6-DoF trajectory tracking. The structure of Quad3DV is simpler than other published multirotors with BTAUs, and can hover at $90^\circ$ pitch with higher flight efficiency. First, the 6-DoF multibody dynamic model of the whole system is given considering variable inertia parameters, gyroscopic effects of the BTAU, and coupling effects of translational and rotational motions. Second, based on this model, an antidisturbance Lyapunov stable 6-DoF trajectory tracking controller is designed. Extended state observer is adopted to estimate the unmodeled error and external disturbances. Third, an admissible-wrench-space-optimal geometric control allocation method is proposed, which fully exploits the controllability and attitude accessibility of Quad3DV . Fourth, simulations were carried out in the high fidelity dynamics simulation software to compare with other controllers. Finally, flight experiments were conducted to verify the effectiveness of the whole system.