Dr. Yanjie CHEN

Non-cooperative target herding is one of the concerns in the robotics field. For the non-cooperative target herding problem, a general planning framework is proposed to compel the target to the destination by a mobile robot team with pursuit, encirclement, and guidance operations. In the proposed planning framework, the encirclement strategy and guidance strategy are designed to deal with the unpredictability of the target. Firstly, the mobile robots approach the randomly moving target in the pursuit process. Secondly, the encirclement strategy is applied in the encirclement process to enable the mobile robot team to encircle the non-cooperative target. Finally, the guidance strategy is employed in the guidance process to enable the mobile robot team to form a favorable encirclement formation and compel the non-cooperative target to the destination. During the herding task, the mobile robots encircle the target or other mobile robots in a satellite-like motion. Moreover, two queues (orbiters and wanders) are maintained to change the formation adaptively, improving the flexibility of the framework. Various environments are designed to verify the effectiveness of the planning framework both in simulations and real-world experiments. Furthermore, the proposed framework is a general platform having great potential for various applications, where the numbers of mobile robots in the herding team are changeable, and different planning algorithms can be integrated into the framework.

This study proposes an image-based visual servoing (IBVS) method based on a velocity observer for an unmanned aerial vehicle (UAV) for tracking a dynamic target in Global Positioning System (GPS)-denied environments. The proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target. A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is proposed. The depth model ensures image feature accuracy and image trajectory smoothness in rotating target tracking. The relative velocities of the UAV and the dynamic target are estimated using the proposed velocity observer. Thanks to the velocity observer, translational velocity measurements are not required, and the control chatter caused by noise-containing measurements is mitigated. An integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the anti-disturbance ability. The stability of the velocity observer and IBVS controller is analyzed using the Lyapunov method. Comparative simulations and multistage experiments are conducted to illustrate the tracking stability, anti-disturbance ability, and tracking robustness of the proposed method with a dynamic rotating target.

An adaptive sliding-mode disturbance observer (ASMDO)-based finite-time control scheme with prescribed performance is proposed for an unmanned aerial manipulator (UAM) under uncertainties and external disturbances. First, to take into account the dynamic characteristics of the UAM, a dynamic model of the UAM with state-dependent uncertainties and external disturbances is introduced. Then, note that a priori bounded uncertainty may impose a priori constraint on the system state before obtaining closed-loop stability. To remove this assumption, an ASMDO with a nested adaptive structure is introduced to effectively estimate and compensate the external disturbances and state-dependent uncertainties in finite time without the information of the upper bound of the uncertainties and disturbances and their derivatives. Furthermore, based on the proposed ASMDO, the finite-time control scheme with the prescribed performance is presented to ensure finite-time convergence and implement the specified transient and steady-state performance. The Lyapunov tools are utilized to analyze the stability of the proposed controller. Finally, the correctness and performance of the proposed controller are illustrated through numerical simulation comparisons and outdoor experimental comparisons.

An image-based visual servoing (IBVS) control strategy is proposed for the unmanned aerial manipulator (UAM) system to track and grasp a moving target. Specifically, a robust-adaptive velocity observer is designed to estimate the relative velocity between the tracked target and the UAM platform. Based on the velocity observer, an IBVS controller using onboard camera of the UAM platform is proposed for moving target tracking without velocity measurement. Then, the barrier Lyapunov function is introduced into the UAM platform IBVS controller to ensure the safety of target tracking. Besides, another virtual camera is constructed on manipulator end-effector to compensate for the tracking error of the UAM platform. As a benefit, the eye-to-hand onboard camera ensures the global view of the UAM, and the eye-in-hand virtual camera of the manipulator ensures the accuracy of the grasping task. Finally, the stability of the proposed IBVS control strategy is analyzed through Lyapunov theory. The comparative simulations are provided to illustrate the target tracking performance of the proposed method. The experimental results demonstrate that the proposed method can be applied to the UAM with a low-cost sensor suite to realize the tasks of tracking and grasping a moving target.

This article presents a planning method, namely sampling-enhanced exploration tree (SET), to improve computational efficiency in restricted environments while guaranteeing high-quality performance. The core of SET is sampling-enhanced exploration, which consists of critical areas identification, guiding-exploration, and rectifying-exploration. In the critical areas identification phase, the restricted areas are identified based on the distribution of the hybrid samples. Next, the critical samples in restricted areas are selected as the origins of the sampling-enhanced exploration. In the guiding-exploration phase, the sampling-enhanced exploration starts from the origins and marches quickly with the guidance of the leader-samples to capture the spatial feature and connectivity of the restricted areas. The spatial information provides essential guidance for efficient biased sampling. In the rectifying-exploration phase, the directions of sampling-enhanced exploration are rectified to transit the problematic areas and supplement samples. Theoretical analysis is provided to shed light on the properties of SET. Moreover, the generality and effectiveness of SET are verified through a series of mobile robot simulations and real-world experiments.

A POMDP method information entropy determinized sparse partially observation tree (IE-DESPOT) is proposed to explore a high-quality solution and efficient planning in unknown environments. First, a novel sample method integrating state distribution and Gaussian distribution is proposed to optimize the quality of the sampled states. Then, an information entropy based on sampled states is established for real-time reward calculation, resulting in the improvement of robot exploration efficiency. Moreover, the near-optimality and convergence of the proposed algorithm are analyzed. As a result, compared with general-purpose POMDP solvers, the proposed algorithm exhibits fast convergence to a near-optimal policy in many examples of interest. Furthermore, the IE-DESPOT's performance is verified in real mobile robot experiments.

This article investigates the active physical interaction problem of an aerial manipulator in terms of capability, reliability, and costs. Then, an active physical interaction control architecture is presented for the aerial manipulator to achieve both stable motion and interaction behaviors with external wrench estimation. Specifically, an external wrench estimator in absence of the acceleration and wrench measurements is designed to regulate the interaction point of the aerial manipulator with a minimal sensor condition. Next, a force tracking impedance control strategy with variable stiffness is presented to guarantee the contact stability and force tracking of the aerial manipulator with uncertain contact targets. Further, utilizing the knowledge of prescribed performance and terminal sliding mode surface, a pose controller is proposed to implement the dynamic response speed and accuracy control of the aerial manipulator, which provides a prerequisite for the realization of reliable physical interaction tasks. The stability of the proposed control architecture is analyzed through Lyapunov tools. Moreover, the feasibility and performance of the proposed control architecture are validated via simulations and real-world contact experiments.

This article proposes a visual servo control method based on image dynamics for an unmanned aerial manipulator (UAM) combining an unmanned aerial vehicle (UAV) with a multi degree-of-freedom onboard manipulator. First, taking into account the dynamic characteristics of the coupled UAM, the dynamic model of the coupled system is derived. Then, based on the perspective projection model, the appropriate image features are defined on the virtual image plane to obtain decoupled image feature dynamics. Combining image features dynamics and UAM dynamics, the position and attitude controller of the UAM flying platform with the center of gravity compensation is derived. Furthermore, the image feature motion is further applied to the positioning control of the end-effector of the onboard manipulator. In addition, the UAM motion distribution strategy is designed to coordinate the movement between the UAM flying platform and the onboard manipulator. The simulation results illustrate the performance of the proposed visual servo control based on image dynamics. As a result, the trajectories of the feature points are smooth in the real image plane, which means the UAM movement is stable. Finally, the experimental results in an outdoor environment verify the practicability and effectiveness of the proposed visual servo control method for autonomous operations. The proposed control method manages the UAM from an unknown initial position to the desired position, which can be applied to various types of aerial operations missions such as object transmission.

This article introduces a secure tunnel fast marching tree motion planning algorithm (ST-FMT*) to provide a secure and optimal path quickly for a mobile robot. The proposed ST-FMT* consists of preprocessing and exploring procedures, which are responsible for establishing a secure tunnel and optimizing the path, respectively. In the preprocessing process, the generalized Voronoi graph is adopted to build an equidistant roadmap and generates an initial collision-free solution rapidly. Then, a secure tunnel is established via the minimum distance from the obstacles to the initial solution to facilitate the concentration of sampling. In the exploration process, the FMT* with uniform sampling within the secure tunnel is utilized to find the optimal solution with high computational efficiency. The theoretical analyses of the ST-FMT* are provided. In a series of scenarios evaluation, the ST-FMT* exhibits fast convergence to the optimal solution in different environments. Besides, the effectiveness of the ST-FMT* is verified in a transportation experiment using a Turtlebot2 mobile robot.

This article presents a trajectory tracking control method for the unmanned aerial manipulator robot system (UAMRS) under model uncertainty and unknown disturbances. More specifically, a low-complexity prescribed performance controller is proposed to effectively reduce the design complexity and achieve the prescribed transient and steady-state performance. First, the dynamics model of the UAMRS is analyzed and modeled, where the unmeasured internal interaction generated by the coupling effect and the random environmental disturbances are considered simultaneously. Then, utilizing the property of prescribed performance, the UAMRS withmodel uncertainty and external disturbances can guarantee preferable trajectory tracking responses, where the nonlinear disturbance observer is used to estimate and compensate uncertainties and external disturbances. Moreover, the proposed controller defined by simple expressions does not require accurate knowledge of the UAMRS, which is of low complexity and can effectively reduce the amount of calculation. The stability of the proposed controller is analyzed. Finally, the performances of the proposed scheme are demonstrated by the numerical simulation comparisons and real-world experiments, where a quadrotor with a 3-DOF onboard active manipulator is adopted in outdoor experimental validations.

This article presents the problem of autonomous control of an unmanned aerial manipulator (UAM) developed for operation with unknown disturbances, wherein the disturbances from the coupling effect between the UAM and the external environment need to be considered. Regarding the coupling force as a disturbance to the entire UAM system, an adaptive prescribed performance control (APPC) scheme utilizing the knowledge of prescribed performance is proposed to guarantee the transient and steady-state performance responses. Also, an adaptive law is designed to estimate the upper boundary parameters of the UAM system uncertainties and disturbances, wherein the restrictive constant boundary assumptions and the prior information of the upper bound are not required in the controller design. Furthermore, to enable safe manipulation in a realistic situation, an endeffector trajectory generation method is presented satisfying the joint angle limitation. For the validation of the proposed method, the simulation results of numerical simulation comparisons are shown. Moreover, experimental scenarios including stable flight and simulated co-work with humans in complex environments are designed to verify the proposed method.

The refined aerial operations of the unmanned aerial manipulator (UAM) have been extensively studied for the last decades. Usually, UAM operations are accompanied by several phases, such as free flight, contact operations, and separation. A great challenge is proposed for switching operations in different environments and the high-precision contact force requirements of UAM control, so this paper conducts control stability research in the case of dynamic differences between free flight and contact operation for the UAM system. First, a hybrid force/position control strategy is proposed for switchable UAM system, among them, the adaptive sliding mode control method based on the interference observer is utilized in the free flight phase, and the adaptive impedance force control method is employed in the contact operation phase, where the adaptive estimation method is designed to perform on-board manipulator contact force estimation. Then a robust adaptive control strategy is proposed for the attitude loop to compensate for the torque disturbance generated during the contact operation phase. Meanwhile, the stability of the switching system is analyzed through the continuous Lyapunov function to prove the stability of the switching process. Finally, the effectiveness and superiority of the proposed schemes are verified through contact operation simulations and experiments.

In this paper, considering the control difficulty of the unmanned aerial manipulator (UAM) interacting with environments, a force analysis during gliding grasping and a hybrid force/position control strategy are proposed for the UAM to enhance control performances during dynamic gliding grasping respectively. First, the instantaneous contact force during the gliding grasping is analyzed by the impulse and momentum theorem, and some factors affecting grasping performance are considered to complete an analysis of grasping force including the irregular shape of the object, the object scrolling, and geometrically asymmetric grasping. Meanwhile, the mass of the grasping object and the inertia tensor are considered unknown bounded items. As a benefit, an accurate dynamics model of the UAM gliding grasping is guaranteed. Second, a hybrid force/position controller based on an adaptive neural network estimator is adopted for UAM to overcome both internal disturbances and external disturbances. The proposed method stability is analyzed through the Lyapunov stability theory. Finally, through a dynamic gliding grasping simulation, the effectiveness and superiority of the proposed scheme are verified.