Zhichao Yang

The unmanned aerial manipulator (UAM) system, with its ability to actively interact with the environment, has significant potential for applications in infrastructure inspection and disaster relief. However, the majority of current research focuses on interactive scenarios near the system’s equilibrium point or at low speeds, limiting the utilization of the aerial vehicle’s maneuverability advantages. In addition, the unique physical characteristics of hammering tasks present challenges to both the safety and effectiveness of aerial operations. To address the aforementioned problem, this article proposes an impact mitigation strategy for aerial manipulator hammering tasks, aiming to ensure the safety of the UAM system while achieving effective hammering operation. Specifically, this strategy divides the UAM hammering task into hover, dive, and hammer phases. During the dive phase, the aerial vehicle’s maneuverability is exploited for accelerated descent, while the manipulator adjusts the end effector’s attitude to suit the hammering scenario requirements. The planning method within the strategy considers the desired velocity and attitude of the end effector at the moment of hammering, generating smooth trajectories for the aerial vehicle and manipulator. Furthermore, a manipulator controller with active/passive work modes is designed to mitigate collision impulses without the need for any specialized devices. During the hammering phase, external and internal impulse modeling and analysis are conducted, resulting in a theoretical assurance of the maximum pre-hammering end effector velocity for system safety. Multiple sets of experimental data demonstrate that the proposed strategy performs well in hammering tasks on surfaces with inclinations and materials of different hardness, effectively achieving hammering with the UAM.

Gaussian process regression (GPR) models are becoming increasingly tightly integrated into robotic systems, particularly in the context of robot model predictive control (MPC) operating in complex environments. Because data generated by robots are typically collected online and exhibits heteroscedasticity (i.e., the noise variance depends on the input), traditional GPR may not be suitable. Thus, an incremental heteroscedastic GPR (IHGPR) method is proposed, which takes advantage of incremental sparse spectrum GPR (I-SSGPR) and the framework of improved most likely heteroscedastic GPR (improved MLHGPR). The predictive distribution is not only in an explicit form but also differentiable, rendering a plug-and-play solution for optimization-based control. The efficacy of the proposed approach is demonstrated through a series of empirical evaluation experiments, highlighting its time efficiency and capacity to accurately capture heteroscedastic signals. To illustrate its versatile applicability in robotic system control, we integrate IHGPR into a robust MPC (RMPC) method to online fit the state- and input-dependent heteroscedastic stochastic disturbances and present the applicability and efficiency through simulation results.

Cable-suspended aerial transportation systems are employed extensively across various industries. The capability to flexibly adjust the relative position between the multirotor and the payload has spurred growing interest in the system equipped with variable-length cable, promising broader application potential. Compared to systems with fixed-length cables, introducing the variable-length cable adds a new degree of freedom. However, it also results in increased nonlinearity and more complex dynamic coupling among the multirotor, the cable and the payload, posing significant challenges in control design. This paper introduces a backstepping control strategy tailored for aerial transportation systems with variable-length cable, designed to precisely track the payload trajectory while dynamically adjusting cable length. Then, a cable length generator has been developed that achieves online optimization of the cable length while satisfying state constraints, thus balancing the multirotor's motion and cable length changes without the need for manual trajectory planning. The asymptotic stability of the closed-loop system is guaranteed through Lyapunov techniques and the growth restriction condition. Finally, simulation results confirm the efficacy of the proposed method in managing trajectory tracking and cable length adjustments effectively.

The unmanned aerial manipulator system, consisting of a multirotor UAV (unmanned aerial vehicle) and a manipulator, has attracted considerable interest from researchers. Nevertheless, the operation of a dual-arm manipulator poses a dynamic challenge, as the CoM (center of mass) of the system changes with manipulator movement, potentially impacting the multirotor UAV. Additionally, unmodeled effects, parameter uncertainties, and external disturbances can significantly degrade control performance, leading to unforeseen dangers. To tackle these issues, this paper proposes a nonlinear adaptive RISE (robust integral of the sign of the error) controller based on DNN (deep neural network). The first step involves establishing the kinematic and dynamic model of the dual-arm aerial manipulator. Subsequently, the adaptive RISE controller is proposed with a DNN feedforward term to effectively address both internal and external challenges. By employing Lyapunov techniques, the asymptotic convergence of the tracking error signals are guaranteed rigorously. Notably, this paper marks a pioneering effort by presenting the first DNN-based adaptive RISE controller design accompanied by a comprehensive stability analysis. To validate the practicality and robustness of the proposed control approach, several groups of actual hardware experiments are conducted. The results confirm the efficacy of the developed methodology in handling real-world scenarios, thereby offering valuable insights into the performance of the dual-arm aerial manipulator system.

The aerial manipulator, composed of a multirotor and a robotic arm, could provide extra active operation capability and has the potential for applications in both military and civilian fields. Due to the complex working scenarios, planning a safe and reliable motion trajectory for aerial manipulators is of great importance. To achieve target grasping with aerial manipulators in complex environments, a coupled motion planning method for the aerial manipulator is proposed in this article, which conducts the motion planning for the multirotor and the robotic arm simultaneously. Specifically, the aerial manipulator is considered as enclosed by a convex polyhedron other than a big sphere so as to reduce the conservatism of the trajectory. Furthermore, the trajectory of the aerial manipulator could be represented by a set of polynomial functions of the flat outputs, including the position and the yaw angle of the multirotor and the joint angles of the robotic arm. Utilizing polynomial-based trajectory representation for both the multirotor's and the robotic arm's states simplifies the optimization problem and ensures dynamic feasibility. The simulation and experimental results show the effectiveness of the proposed method. Unlike the other works that only consider the position of the end-effector, the proposed method also takes into account the orientation of the end-effector as well, resulting in improved performance in grasping tasks.

Cable-suspended aerial transportation systems are receiving increased attention due to their simple structure and flexible maneuverability. In most existing studies, the hook and payload are often simplified as a single point mass, disregarding the hook’s movement. However, in practical applications, the payload is suspended from the unmanned aerial vehicle (UAV) by a hook, resulting in a double-pendulum dynamic effect, where both the payload and the hook exhibit swinging motions. This effect causes the double-pendulum aerial transportation system to exhibit strong nonlinearity and state coupling, posing significant challenges for controller design. To address this issue, an adaptive trajectory tracking controller based on the adaptive dynamic programming (ADP) method is proposed for the double-pendulum aerial transportation system under external disturbances. Specifically, by defining the actor neural network (NN) and the critic NN, the optimal control input term is first obtained. Compensation and robustness terms are then introduced to address system uncertainties and external disturbances. The Lyapunov method is employed to rigorously prove the asymptotic convergence of the tracking errors. Comparative simulations and experiments validate the proposed method’s trajectory tracking performance and robustness, underscoring its practical effectiveness.

Aerial transportation systems have demonstrated unique application value in various fields, particularly in logistics transportation, and have garnered significant attention recently. Most of the existing researches on cable-suspended transportation system regard the payload as a point mass, neglecting the attitude of the payload. To achieve the attitude control of nonpoint mass payload during transportation, that can significantly expand the application scope, this article develops the omni-directional aerial transportation system, where a fully actuated UAV is utilized instead of the underactuated UAV, and a bar-shaped payload is slunged by two cables. However, compared with the single-cable suspension system, this system manifests more complex dynamic coupling and nonlinearity. Specifically, the UAV's attitude subsystem is coupled with the swing of payload. To deal with these problems, an antiswing control approach is presented, where an enhanced-coupling signal is constructed to improve the capability of payload swing elimination. The asymptotic convergence of the equilibrium point is strictly proved by Lyapunov techniques and LaSalle's invariance theorem. Furthermore, two groups of hardware experiments are conducted to validate the effectiveness of the proposed control method. To the best of our knowledge, the omni-directional aerial transportation system and control solution are developed to achieve the attitude control of payload during transportation with single UAV for the first time in this article.