Yushu Yu

The compound autonomous aircraft, comprising a carrier Unmanned Aerial Vehicle (UAV) and multiple sub land-air amphibious vehicles (LAVs), has recently been developed due to its endurance and maneuverability. However, the time-varying airflow disturbances generated by the flying carrier UAV pose significant challenges during the landing process of the LAVs. In this paper, a stability augmentation landing control (SALC) system incorporating adaptive reference models was proposed. The controller integrated an adaptive output predictor and a reconfigurable control law. The output predictor was designed using variable reference models to extend the expected robust stability boundary under large-scale, time-varying disturbances. Further, it achieved a balance between fast disturbance rejection and system stability. Based on this predictor, a reconfigurable control law was developed to compensate for disturbances and uncertainties. Theoretical analysis proved that the estimation errors were bounded. Both bench test and outdoor test demonstrated the superiority of the proposed control strategy. The experiment results showed the SALC-equipped LAV achieved stable landing under disturbances induced by the carrier UAV. Compared with other conventional controllers, the proposed SALC strategy reduced the mean landing deviation by up to 58.9%.

Ducted fan flying robots with robotic arms can perform physical interaction tasks in complex environments such as indoors. However, the coupling effects between the aerial platform, the robotic arm, and physical environment pose significant challenges for the robot to accurately approach and stably contact the target. To address this problem, we propose a unified control framework for a ducted fan flying robot that encompasses both flight planning and physical interaction. This contribution mainly includes the following: (1) A nonlinear model predictive control (NMPC)-based trajectory optimization controller is proposed, which achieves accurate and smooth tracking of the robot’s end effector by considering the coupling of redundant states and various motion and performance constraints, while avoiding potential singularities and dangers. (2) On this basis, an easy-to-practice hierarchical control framework is proposed, achieving stable and compliant contact of the end effector without controller switching between the flight and interaction processes. The results of experimental tests show that the proposed method exhibits accurate position tracking of the end effector without overshoot, while the maximum fluctuation is reduced by up to 75.5% without wind and 71.0% with wind compared to the closed-loop inverse kinematics (CLIK) method, and it can also ensure continuous stable contact of the end effector with the vertical wall target.

Gesture recognition, an important method for Human-Drone Interaction (HDI), is often constrained by sensor limitations, such as sensitivity to lighting variations and field of view (FoV) restrictions. This paper proposes a real-time drone control system that integrates multimodal fusion gesture recognition with active perception control to overcome these challenges. We constructed a diversified arm gesture dataset and designed a lightweight, one-stage point cloud and image features fusion model, Adaptive Gate Fusion Network (AGFNet), for real-time inference on embedded devices. We applied motion compensation to mitigate delay errors caused by point clouds accumulation and network inference during movement, and fused the drone's velocity data and detection results using the Extended Kalman Filter (EKF) to enhance real-time performance. This enabled active perception through the optimization of the sensor's FoV using perception-aware Model Predictive Control (PAMPC). Experimental results demonstrate that the proposed model achieves a threefold improvement in inference speed compared to the baseline, reaching 97.93% mAP on the test set and outperforming single-sensor networks by approximately 5%. Real-world testing further confirms the system's applicability and effectiveness in indoor environments. The code is available at: https://github.com/fangweicheng6/Gesture-HDI.git.

The Integrated Aerial Platform (IAP) uses multiple quadrotor sub-vehicles, acting as independent thrust generators, connected to a central platform via passive joints. This setup allows the sub-vehicles to collectively apply forces and torques to the central platform, achieving full six-degree-of-freedom (6-DoF) motion through coordinated thrust and posture adjustments. The IAP's modular design offers significant advantages in terms of mechanical simplicity, reconfigurability for diverse scenarios, and enhanced mission adaptability. This letter presents a comprehensive framework for IAP modeling and optimal design. We introduce a “design matrix” that encapsulates key architectural parameters, including the number of sub-vehicles, their spatial configuration, and the types of passive joints used. To improve control performance and ensure balanced wrench generation capabilities, we propose an optimized design strategy that minimizes the condition number of this design matrix. Two distinct IAP configurations were optimally designed based on two typical application scenarios. The efficacy of the proposed optimization methodology was subsequently validated through comparative analysis against unoptimized platforms. Moreover, the full actuation capability of the IAP was empirically confirmed via extensive simulations and real-world flight experiments, which also demonstrated its operational performance through direct wrench control experiment.

This paper focuses on a class of 5-degrees-of-freedom (5-DOF) aerial platforms, particularly the Passively Articulated Dual UAVs (PADUAVs). These platforms have the potential to achieve omnidirectional motion, as their joints are free from position constraints. However, PADUAVs encounter singularity issues in certain configurations. To address this challenge, we propose a novel singularity-avoidance control strategy. The approach begins with an analysis of the flat outputs of the 5-DOF aerial system. Based on this analysis, we design a careful allocation strategy that maps position control to attitude control via the flat outputs. A variable intermediate attitude is introduced to ensure that this allocation remains singularity-free across all configurations of the 5-DOF aerial vehicle. The stability of the proposed controller is rigorously proven. We then apply the proposed control method to the PADUAV platform, providing detailed modeling, analysis, and dynamic decoupling of the system. Due to the presence of additional sub-vehicle dynamics in the PADUAV, an auxiliary attitude allocation module is also developed. The proposed position and attitude control allocation strategies enable the controller to maintain singularity-free stability across all configurations. Finally, we implement a 5-DOF tracking control strategy specifically tailored for the PADUAV. Numerical simulations validate the effectiveness of the proposed approach, demonstrating its robustness and reliability in aerial manipulation tasks.

Integrated Aerial Platforms (IAPs), consisting of multiple interconnected aircraft, offer a promising framework for aerial manipulation tasks by enhancing localization accuracy and reliability. Unlike aerial swarms, the interconnected nature of IAPs allows for exploiting physical constraints among aircraft to improve positioning and navigation systems. In this paper, we present an advanced decentralized multi-aircraft visual-inertial-range-physical odometry system that considers the position, velocity, and attitude constraints inherent to IAPs. By tightly fusing visual-inertial-range odometry and Ultra-Wideband (UWB) with kinematic constraints, we optimize odometry accuracy through the use of novel constraint-based methods. Our algorithm’s performance is validated on simulated datasets and self-collected datasets, with flight experiments conducted on the IAP, demonstrating a significant improvement with a 28.7% reduction in drift over the baseline on real-world datasets and a 24.5% reduction on simulation datasets.

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm.

This paper presents a simulator framework tailored for Integrated Aerial Platforms (IAPs) using multiple quadrotors. Our framework prioritizes photo and contact fidelity, achieved through a modular design that balances rendering and dynamics computation. Key features include: i) support for diverse IAP configurations; ii) a customizable physics engine for realistic motion and contact simulation for aerial manipulation; and iii) Unreal Engine 5 for lifelike rendering, with sensor designs for visual-inertial SLAM positioning simulation. We showcase our framework’s versatility through a range of scenarios, including trajectory tracking for both fully and under-actuated IAPs, peg-in-hole and direct wrench control tasks under external wrench influence, tightly-coupled SLAM positioning with physical constraints, and air docking task training and testing using offline-to-online reinforcement learning. Furthermore, we validate our simulator framework’s fidelity by comparing results with real flight data for trajectory tracking and direct wrench control tasks. Our simulator framework promises to be valuable for developing and testing integrated aerial platform systems for aerial manipulation.

Small-scale autonomous aerial vehicles (AAVs) are widely used in various fields. However, their underactuated design limits their ability to perform complex tasks that require physical interaction with environments. The fully-actuated Integrated Aerial Platforms (IAPs), where multiple AAVs are connected to a central platform via passive joints, offer a promising solution. However, achieving accurate state estimation for IAPs in GPS-denied environments remains a significant hurdle. In this letter, we introduce a centralized state estimation framework for IAPs with a fusion of odometry and kinematics, using only onboard cameras and inertial measurement units (IMUs). We develop a forward-kinematic-based formulation to fully leverage localization information from kinematic constraints. An online calibration method for kinematic parameters is proposed to enhance state estimation accuracy with forward kinematics. Additionally, we perform an observability analysis, theoretically proving that these kinematic parameters are fully observable under conditions of fully excited motion. Dataset and real-world experiments on a three-agent IAP prototype confirm that our method improves localization accuracy and reduces drift compared to the baseline.