Aerial robots have demonstrated significant potential in suspended cargo transportation, especially in industries such as logistics and food delivery. Due to the underactuated and nonlinear dynamics of the cable-suspended system, directly tracking a given trajectory with a multicopter without modifying its controller often leads to significant payload swing. This compromises the safety and stability of the cargo. To address the aforementioned issue, this paper proposes an online trajectory refinement method for a variable-length cable-suspended aerial transportation robot, independent from the control layer. By incorporating payload swing angle information, the reference trajectory is refined in real-time, effectively suppressing payload oscillations during transportation. Specially, Lyapunov techniques and LaSalle’s invariance theorem are employed to rigorously guarantee the feasibility of the designed trajectory refinement scheme. Finally, hardware experiments are conducted to validate the effectiveness and superiority of the proposed method. The results demonstrate that the refined trajectory not only enables precise positioning of the multicopter, but also effectively suppresses payload oscillations during transportation, significantly enhancing the safety and reliability of the aerial cargo delivery.

Landing a quadrotor on a mobile platform moving with various unknown trajectories presents special challenges, including the requirements of fast trajectory planning/replanning, accurate control, and the adaptability for different target trajectories, especially when the platform is non-cooperative. However, previous works either assume the platform moves along a predefined trajectory or decouple planning from control which may cause a delay in tracking. In this work, we integrate planning and control into a unified framework and present an efficient off-policy Meta-Reinforcement Learning (Meta-RL) algorithm that enables a quadrotor (agent) to land on a mobile platform with various unknown trajectories autonomously. In our approach, we disentangle task-specific policy parameters by a separate adapter network to shared low-level parameters and learn a probabilistic encoder to extract common structures across different tasks. Specifically, during meta-training, we sample different trajectories from the task distribution, and then the probabilistic encoder accumulates the necessary statistics from past experience into the latent variables that enable the policy to perform the task. At meta-testing time, when the quadrotor is faced with an unseen trajectory, the latent variables can be sampled according to past interactions between the quadrotor and the mobile platform and held constant during an episode, enabling rapid trajectory-level adaptation. We assume similar tasks share a common low-dimensional structure in the representation of the policy network and the task-specific information is learned in the head of the policy. Accordingly, we further propose a separate adapter net as a supervised learning problem. The adapter net learns the weights of the policy’s output layer for each meta-training task given by the environment interactions from the agent. When adapting to a new task during meta-testing, we fix the shared model layers and predict the head weights for the new task using the trained adapter network. This ensures that the pretrained policy can efficiently adapt to different tasks, which boosts the out-of-distribution performance. Our method can directly control the pitch, roll, yaw angle, and thrust of the quadrotor, yielding a fast response to the trajectory change. Simulation results show the superiority of our method both in success rate and adaptation efficiency over other RL algorithms on meta-testing tasks. The real-world experimental results compared with traditional planning and control algorithms demonstrate the satisfactory performance of our autonomous landing method, especially its robustness in adapting to unknown dynamics.Note to Practitioners—Given the challenge posed by the motion uncertainty when a quadrotor lands on a mobile platform with an unknown trajectory, there hasn’t been a well-established solution, as far as we know. This paper introduces meta-reinforcement learning, incorporating a latent variable encoder to extract common features from training tasks, and designing an adapter network to enhance the ability of policy networks to adapt to new tasks, thereby enhancing the landing performance of the agent. The proposed method demonstrates promising results in both simulation and experiments.

The unmanned aerial manipulator system has shown great application potential in rotor blade repairing, bridge inspection, and goods delivery. Allowing additional alternatives in tasks, dual-arm always provides more flexibility, versatility, and manipulability compared to a single arm. Unfortunately, the inherent defects of unignorable nonlinearities and complex dynamic coupling between the multirotor and the manipulator have limited the practical application of dual-arm aerial manipulator systems. It is noteworthy that the dynamic coupling between the multirotor UAV (unmanned aerial vehicle) and the dual-arm manipulator is much more complicated than the case of the single-arm, and may degrade the control performance significantly as the CoM (center of mass) of the system changes with the movement of the manipulator. To this end, this paper presents a novel control method based on dual-arm movement compensation. Specifically, the kinematic and dynamic model of the system is first established, based on which the force effect of the manipulator exerting on the multirotor UAV is estimated by the disturbance observer and then compensated. By using Lyapunov techniques, it is proven that the error signal can converge asymptotically. As far as we know, this paper presents the first controller design for dual-arm aerial manipulator systems with rigorous stability analysis. Finally, the effectiveness and robustness of the proposed method are verified through a significant number of comparison experiments, and the results of these experiments demonstrate that the proposed method can reduce the positioning error obviously compared to the comparison methods. Taking the PID method as the benchmark for comparison, it is obvious that the proposed method exhibits the greatest reduction in both maximum and average errors compared to the baseline method, indicating superior control precision than the other comparison methods. For the result of the proposed method in$\bm x$-direction, one can find a substantial reduction ranging from 16.69% to 38.57% for the maximum error, and 22.24% to 45.66% for the mean error. Shifting focus to the$\bm y$-direction, the error reduction for the proposed method is even more remarkable, ranging from 68.10% to 81.80% at maximum, and 66.31% to 86.33% for the mean error. As for the$\bm z$-direction, the error reduction by the proposed method remained significant, ranging from 50.67% to 86.38% at maximum, and with a mean error reduction of 33.11% to 80.39%.Note to Practitioners—This paper is motivated by the problem of executing such tasks as load transportation and coordinate manipulation for aerial robots in flight. By integrating the dual-arm manipulator, the flexibility, versatility, and manipulability of the unmanned aerial manipulator system is further extended. However, the uncertain center of mass of the system during operation may badly increase the control difficulty of the dual-arm aerial manipulator system. Moreover, the strong nonlinearity and complex coupling existing between the multirotor and the manipulator also induce urgently solved problems in practical aerial manipulation tasks. To this end, this paper proposes a novel dual-arm movement compensation based control scheme by utilizing an elaborately designed disturbance observer to deal with the unestimated part of disturbance exerting on the multirotor by arm operation. With rigorous theoretical analysis, the convergence of the error signal is proven. Additionally, groups of hardware experiments further verify the effectiveness and robustness of the suggested control method. In future studies, we will improve the autonomy level of the system by integrating onboard sensors.

By utilizing a suspension cable to connect the payload with the quadrotor, transport tasks can be accomplished while preserving the unmanned aerial vehicle’s agility and maneuverability, particularly in environments that are impassable for ground vehicles. Equipping onboard visual sensors and utilizing image-based visual servoing techniques, the application range of aerial transportation systems is poised to be significantly expanded in scenarios like autonomous landing and goods release. Unfortunately, within the system, there exist multiple layers of dynamic couplings between image features, quadrotor rotation, translation, and payload motion. These intricacies give rise to numerous difficulties in achieving smooth anti-swing transportation. To overcome the aforementioned difficulties, this paper presents the first image-based visual servoing control scheme for the aerial transportation system with variable-length cable. Specifically, the image moments defined on the rotated virtual image plane are taken as the image features, whose dynamics is independent of the quadrotor rotational motion. Subsequently, a generalized virtual image feature signal is introduced by organically combining the cable length and payload swing angles with the image feature, which is further exploited in the anti-swing control scheme design. The equilibrium point of the overall closed-loop system is proved to be asymptotically stable through Lyapunov techniques and LaSalle’s Invariance Theorem. Hardware experiments are conducted on a self-built aerial transportation platform to verify the proposed controller’s basic and functional performance in terms of rapid anti-swing and accurate target position and cable length tracking. Note to Practitioners—This paper is motivated by the requirement to improve the autonomy level and payload swing suppression ability of the aerial transportation system through visual servoing techniques. By installing onboard monocular camera and the cable length adjustment mechanism, the application scope of the aerial transportation system can be significantly expanded. However, due to the “double” underactuated characteristic, the visual features couple with both the quadrotor motion and the payload motion, hence, it is quite challenging to realize visual servoing control for cable-suspended aerial transportation systems with simultaneous payload swing suppression and quadrotor positioning. Accounting for the foregoing problems, this paper proposes an image-based visual servoing anti-swing control scheme. With the elaborately constructed generalized virtual image feature signal, the designed controller could improve the anti-swing ability with a completed theoretical analysis. Furthermore, two groups of hardware experiments are conducted to validate the effectiveness of the suggested control method. In future studies, we intend to design more effective control scheme for payload delivery issue with consideration of the visibility of the mobile platform.

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. In addition, 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.

With excellent maneuver performance and flexibility, quadrotor unmanned aerial vehicles (UAVs) are widely used in aerial transportation. However, the aerial transportation system with dual quadrotors exhibits high degrees of freedom, strong nonlinearities, and complex state couplings, which makes it more difficult to realize simultaneous quadrotor positioning and cargo swing suppression. Compared with the traditional description of cargo swing dynamics with four angles in the previous work, the spatial swing angle is introduced in a more intuitive way to reflect the swing dynamics of the cargo. On this basis, the dynamic model of the system is established according to Lagrange's equations. Then, a nonlinear adaptive controller is proposed, in which a dynamic compensation term is introduced to compensate for the lateral forces along the cables, and a spatial swing angle-related term is designed to enhance cargo swing damping. Meanwhile, considering the influence of unknown air resistance on quadrotors and cargo during transportation, an adaptive term is applied. Subsequently, Lyapunov techniques and LaSalle's invariance principle are used to prove the stability of the closed-loop system. Finally, based on the self-built general experimental platform, both indoor and outdoor experiments have been carried out to validate the practicability and effectiveness of the proposed method.

Recently, it is becoming increasingly possible to apply aerial transportation systems to real-world applications. However, current research works on cable-suspended transportation systems present practical limitations due to the fixed-length cable. With the introduction of the cable adjustment mechanism, various complicated tasks, such as limited space crossing, offshore sample collection, and even landing the payload on a mobile platform, can be accomplished by actively changing the distance between the quadrotor and the payload. In order to complete the aforementioned tasks, a trajectory tracking control method is in urgent need for the variable-length-cable-suspended aerial transportation systems. To this end, an adaptive tracking control approach with the consideration of unknown resistance coefficients is designed in this article. Subsequently, Lyapunov techniques and Barbalat's Lemma are utilized to prove the convergence for the equilibrium point of the closed-loop system. Finally, hardware experiments are meticulously conducted based on a self-built experimental platform, which verify the satisfactory performance of the proposed method in antiswing aerial transportation and payload landing onto the mobile platform.