This article presents a trajectory tracking control method for the unmanned aerial manipulator robot system (UAMRS) under model uncertainty and unknown disturbances. More specifically, a low-complexity prescribed performance controller is proposed to effectively reduce the design complexity and achieve the prescribed transient and steady-state performance. First, the dynamics model of the UAMRS is analyzed and modeled, where the unmeasured internal interaction generated by the coupling effect and the random environmental disturbances are considered simultaneously. Then, utilizing the property of prescribed performance, the UAMRS with model uncertainty and external disturbances can guarantee preferable trajectory tracking responses, where the nonlinear disturbance observer is used to estimate and compensate uncertainties and external disturbances. Moreover, the proposed controller defined by simple expressions does not require accurate knowledge of the UAMRS, which is of low complexity and can effectively reduce the amount of calculation. The stability of the proposed controller is analyzed. Finally, the performances of the proposed scheme are demonstrated by the numerical simulation comparisons and real-world experiments, where a quadrotor with a 3-DOF onboard active manipulator is adopted in outdoor experimental validations.

Hazy images have limited visibility and low contrast. The degradation is expressed by transmission map, which is one of the most important estimates of single image dehazing. Transmission map estimation is an underconstraint problem, and lots of priors have been proposed. Among them, the dark channel prior is widely recognized. However, traditional methods have not fully exploited its power due to improper assumptions or operations, which cause unwanted artifacts. The postrefinement algorithms employed to remove these artifacts in turn undermine the merits of the prior. In this letter, a novel method for estimating transmission map by energy minimization is proposed to solve this problem. The energy function combines the dark channel prior with piecewise smoothness. The method is compared to the state-of-the-art methods and shows outstanding performance.

This article proposes a visual servo control method based on image dynamics for an unmanned aerial manipulator (UAM) combining an unmanned aerial vehicle (UAV) with a multidegree-of-freedom onboard manipulator. First, taking into account the dynamic characteristics of the coupled UAM, the dynamic model of the coupled system is derived. Then, based on the perspective projection model, the appropriate image features are defined on the virtual image plane to obtain decoupled image feature dynamics. Combining image features dynamics and UAM dynamics, the position and attitude controller of the UAM flying platform with the center of gravity compensation is derived. Furthermore, the image feature motion is further applied to the positioning control of the end-effector of the onboard manipulator. In addition, the UAM motion distribution strategy is designed to coordinate the movement between the UAM flying platform and the onboard manipulator. The simulation results illustrate the performance of the proposed visual servo control based on image dynamics. As a result, the trajectories of the feature points are smooth in the real image plane, which means the UAM movement is stable. Finally, the experimental results in an outdoor environment verify the practicability and effectiveness of the proposed visual servo control method for autonomous operations. The proposed control method manages the UAM from an unknown initial position to the desired position, which can be applied to various types of aerial operations missions such as object transmission.

Unmanned aerial manipulator (UAM) is usually a combination of a quadrotor and a robotic arm that can exert active influences on the environments. The control problems of the UAM system include model uncertainty caused by its center of gravity shift and external disturbances from the environments. To handle these two disturbances, a tracking control strategy is proposed for position and attitude control of the UAM in this paper. In particular, the model of the UAM is established considering with center of gravity shift and disturbances from environments. In the position control, both internal disturbances and external disturbances are compensated by using a sliding mode controller. In the attitude control, an adaptive law is designed to estimate internal disturbances, and a disturbance observer is designed to estimate external disturbances. The stability analysis of the proposed controller is provided and the effectiveness of the proposed method is verified in simulation.

This paper devotes to the adaptive globally synchronization within predefined-time of two time-delayed fractional-order chaotic systems. Firstly, through fractional calculus, two novel different fractional-order systems with time-delay are proposed, whose convergence is guaranteed and phase trajectory is given. Secondly, by exploiting the non-negative Lyapunov function and inequality theorem, a novel global predefined-time stability theorem is proposed, which can ensure the settling time tunable. And the upper bound of the settling time estimation is more accurate compared with the classical results. With the help of novel predefined-time stability theorem, two active controllers are designed, namely the fixed-time synchronization controller and predefined-time synchronization controller, to achieve the fixed-time synchronization and the predefined-time synchronization of two different time-delayed fractional-order chaotic systems respectively. Finally, several numerical simulations are presented in order to show the effectiveness of the proposed methods.

This article introduces a secure tunnel fast marching tree motion planning algorithm (ST-FMT*) to provide a secure and optimal path quickly for a mobile robot. The proposed ST-FMT* consists of preprocessing and exploring procedures, which are responsible for establishing a secure tunnel and optimizing the path, respectively. In the preprocessing process, the generalized Voronoi graph is adopted to build an equidistant roadmap and generates an initial collision-free solution rapidly. Then, a secure tunnel is established via the minimum distance from the obstacles to the initial solution to facilitate the concentration of sampling. In the exploration process, the FMT* with uniform sampling within the secure tunnel is utilized to find the optimal solution with high computational efficiency. The theoretical analyses of the ST-FMT* are provided. In a series of scenarios evaluation, the ST-FMT* exhibits fast convergence to the optimal solution in different environments. Besides, the effectiveness of the ST-FMT* is verified in a transportation experiment using a Turtlebot2 mobile robot.