Xiao Liang

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.

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.

Active perception in vision-based robotic manipulation aims to move the camera toward more informative observation viewpoints, thereby providing high-quality perceptual inputs for downstream tasks. Most existing active perception methods rely on iterative optimization, leading to high time and motion costs, and are tightly coupled with task-specific objectives, which limits their transferability. In this paper, we propose a general one-shot multimodal active perception framework for robotic manipulation. The framework enables direct inference of optimal viewpoints and comprises a data collection pipeline and an optimal viewpoint prediction network. Specifically, the framework decouples viewpoint quality evaluation from the overall architecture, supporting heterogeneous task requirements. Optimal viewpoints are defined through systematic sampling and evaluation of candidate viewpoints, after which large-scale training datasets are constructed via domain randomization. Moreover, a multimodal optimal viewpoint prediction network is developed, leveraging cross-attention to align and fuse multimodal features and directly predict camera pose adjustments. The proposed framework is instantiated in robotic grasping under viewpoint-constrained environments. Experimental results demonstrate that active perception guided by the framework significantly improves grasp success rates. Notably, real-world evaluations achieve nearly double the grasp success rate and enable seamless sim-to-real transfer without additional fine-tuning, demonstrating the effectiveness of the proposed framework.