Yi Chai

Dual-arm aerial manipulators enable coordinated and dexterous operations. However, the strong dynamic coupling among the vehicle, the two arms, and the grasped object complicates stable control under safety constraints. This article develops a comprehensive kinematic-dynamic model that explicitly incorporates the manipulated object and proposes an adaptive dynamic programming controller with a critic–actor neural network. A control barrier function is integrated to enforce asymmetric state and safety limits. The proposed controller is capable of managing unknown dynamics and actuator saturation while simultaneously balancing optimality, safety, robustness, and manipulability. Hardware experiments on a self-built dual-arm aerial platform validate the approach on trajectory-tracking tasks with grasped payloads and wind-like perturbations. The proposed controller achieves accurate tracking, maintains stability, and respects safety constraints across operating conditions, outperforming conventional controllers in both tracking accuracy and robustness, thereby supporting practical aerial manipulation.

Leveraging the combined capabilities of multiple unmanned aerial vehicles (UAVs), the collaborative aerial transportation system efficiently manages heavy payload transport tasks. However, in obstacle-rich environments, the system faces complex collision risks at multiple levels: internal coupling collisions, where UAVs may decrease their separation distance during obstacle avoidance, and interference between the UAV, cable, and payload; as well as external collisions, where environmental obstacles impose strict geometric constraints on the system’s movement in narrow spaces. The system must also ensure that both dynamic feasibility and cable tension constraints (to avoid slack or breakage), resulting in a highly nonlinear and nonconvex optimization problem. Furthermore, rapid acceleration or sharp turns during obstacle avoidance can increase the risk of secondary collisions. To address these challenges, this article presents a novel hierarchical collision avoidance framework that integrates obstacle-aware graph searching with constrained cable tension optimization. In the first stage, a 3-D motion-primitive graph search algorithm is proposed to generate smooth, collision-free trajectories, where the search is guided by a heuristic function that jointly optimizes trajectory smoothness and transportation time. In the second stage, cable tension optimization is formulated as a nonlinear problem, ensuring tension feasibility and preventing inter-UAV collisions. Extensive experiments demonstrate that the proposed method improves trajectory smoothness by at least 26.8% and reduces trajectory length by at least 2.4%, compared to baseline methods.

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.

As an essential application of unmanned aerial vehicle (UAV) systems, payload transportation has garnered significant attention in recent years. Collaborative payload transportation utilizing multiple UAVs can effectively increase the payload capacity of the transportation system. Nevertheless, the incorporation of multiple UAVs makes the dynamic model of the transportation system more complex due to the coupled UAV and payload states. In the immediate disaster relief response, the collaborative system is often required to promptly deliver supplies to the target site while avoiding obstacles to ensure the system’s safety. Consequently, devising fast delivery trajectories that avoid collisions for such complicated systems poses a considerable challenge. To this end, a novel trajectory planning method is presented for collaborative transportation systems. Specifically, the dynamic model of the collaborative transportation system is derived by utilizing the Euler–Lagrange method. Then, the trajectory planning problem is formulated as an optimization problem with considerations of dynamics, actuation, safety, and formation constraints. To expedite the optimization process, the collision avoidance safety constraint is constructed using dual variables. The efficacy of this trajectory planning approach is confirmed through multiple real-world flight experiments involving collaborative aerial transportation systems of two and three UAVs.

As a low-cost cargo delivery manner, cable-suspended aerial transportation system is highly regarded by researchers. However, existing works seldom consider the relative distance adjustment between the payload and the multirotor, which greatly limits the application scope, such as tunnel traversing or payload releasing. Additionally, treating the hook and the payload as a single point mass while ignoring the hook effect results in an inaccurate description of the dynamic model. To address the aforementioned problems, the dynamic model of the variable-length cable-suspended aerial transportation system is established accurately through Lagrange's equation with consideration of the motion of the multirotor, the payload, and the hook. Subsequently, an adaptive control method is presented through energy-based analysis, and swing angle related fuzzy rules are established to dynamically adjust the control parameters, which can simultaneously achieve multirotor positioning, payload hoisting/lowering, and hook/payload swing suppression. Moreover, the cable length is constrained within a feasible range by an elaborately designed auxiliary control signal. Lyapunov techniques and LaSalle's invariance theorem are utilized to prove the asymptotic convergence of the closed-loop system. Finally, a series of simulations are conducted to verify the control performance of the designed method.