Hai Yu

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.

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.