An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics of UAVs and the coupling generated by the rigid connection with the manipulator, robustness and a high-precision controller are critical for UAMs. In this paper, we propose a nonsingular global fast terminal sliding mode (NGFTSM) controller for UAMs to track the expected trajectory under the influence of disturbances based on a reasonably simplified UAM system dynamics model. To achieve active anti-disturbance and high tracking accuracy in a UAM system, we incorporate an RBF neural network into the controller to estimate lumped disturbances, including internal coupling and external disturbances. The controller and neural network are derived according to Lyapunov theory to ensure the system’s stability. In addition, we propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with other controllers by simulations. The results show that the proposed controller can effectively enhance the robustness and accuracy of a UAM system with satisfactory convergence. The experimental results also verify the effectiveness of the proposed controller.

Autonomous exploration holds significant application value in tasks like mine exploration and environmental modeling, and personnel search and rescue, effectively boosting task efficiency. Learning-based methods are well-suited for these scenarios. Nevertheless, they suffer from low learning efficiency and challenges in transferring from simulation to reality. To improve efficiency in the exploration process of mobile robots, a novel 3D LiDAR-based autonomous exploration method using deep reinforcement learning in unknown environments is proposed. In particular, a sparse informative graph based on the complementary holes is proposed, which serves as the input of our model. Furthermore, our model is built on the soft actor-critic algorithm, integrating reinforcement learning with a graph-based and self-attention mechanism policy network. Moreover, an enhanced reward in our model is also employed to boost exploration capability. The proposed method was analyzed and validated in both simulation and real-world scenarios, demonstrating superior exploration performance (11.53% in trajectory length, 13.45% in makespan, 30.77% in planning time) than the state-of-the-art (SOTA) method in a 200m×180m self-established scenario. It also demonstrates that our method enhances the efficiency and robustness of autonomous exploration tasks.

Contact force control for Unmanned Aerial Manipulators (UAMs) is a challenging issue today. This paper designs a new method to stabilize the UAM system during the formation of contact force with the target. Firstly, the dynamic model of the contact process between the UAM and the target is derived. Then, a non-singular global fast terminal sliding mode controller (NGFTSMC) is proposed to guarantee that the contact process is completed within a finite time. Moreover, to compensate for system uncertainties and external disturbances, the equivalent part of the controller is estimated by an adaptive radial basis function neural network (RBFNN). Finally, the Lyapunov theory is applied to validate the global stability of the closed-loop system and derive the adaptive law for the neural network weight matrix online updating. Simulation and experimental results demonstrate that the proposed method can stably form a continuous contact force and reduce the chattering with good robustness.

The aerial manipulator is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. In this paper, we propose a composite control scheme considering force and position for the aerial manipulator to operate in steady contact with the environment when influenced by external disturbances. First, a contact force control method without employing the force sensor is obtained on the mechanical relationship of the systems contact with the environment. Second, we regard the systems internal coupling and external disturbance as lumped disturbances and design an extended state observer (ESO) to estimate them. Combined with the disturbance estimation and the nonsingular global fast sliding mode algorithm, a controller derived from the Lyapunov theory is proposed. Finally, we compare the proposed controller with the other four controllers through simulations and actual flight experiments. The results show that the proposed controller can effectively restrain disturbances, reduce convergence time, and guarantee steady contact under external disturbances.