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Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions.

This paper addresses the trajectory tracking control of a Gough–Stewart platform (GS platform) with an uncertain load. The uncertainty of this load leads to external disturbance to the parallel robot, which affects the dynamic coupling among the six degrees of freedom (DOF) and the tracking performance. Even though many researchers focus on improving the system robustness and tracking accuracy, there still exist two main problems: the system’s internal uncertainties, including the modeling, manufacturing, and assembly errors of the parallel robot affect the control accuracy; the uncertain external disturbance varies in an extensive range and reduces the stability and tracking accuracy of the system. Therefore, we propose a novel control methodology: the dynamic Image-based visual servoing (IBVS) Radial basis function neural network (RBFNN) real-time compensation controller. This control considers an acceleration model of visual servoing and performs real-time compensation for the enormous uncertain disturbance from the load with RBFNN. The stability of the proposed controller is fully investigated with the Lyapunov method. Simulations are performed on a GS platform with an uncertain load to test the controller’s performance. It turns out that this controller provides good tracking accuracy and robustness simultaneously.

In this work, an image-based visual servoing control method is proposed for quadrotor tracking ground targets with unknown motion states. Firstly, the dynamic image model containing the parameters of the moving target is established in the virtual image plane by the image moment features. In order to estimate time-variant disturbances during quadrotor flight and unknown motion states of the ground target, a higher-order sliding mode observer is designed. In this context, we propose a finite-time controller and prove the system’s finite-time stability using the Lyapunov theory. Finally, the convergence of the proposed method is verified by numerical simulation, and hardware-in-the-loop simulation evaluates the effectiveness of practical devices. Comparative experimental results demonstrate the superior performance of the proposed method.

Image-Based Visual Servoing (IBVS) systems often suffer from instability due to measurement noise, modeling errors, and external disturbances. To address these issues, this study proposes a Visual Predictive Control framework integrating Radial Basis Function (RBF) and Extended Kalman Filter (EKF) coupled state-disturbance estimation and task-oriented K-means clustering. First, a feedback linearization Model Predictive Control (MPC) law is designed to handle system nonlinearities and physical constraints. Second, a coupled estimation mechanism is established where the EKF suppresses noise while the RBF network learns lumped disturbances. Crucially, to optimize network efficiency, a task-oriented K-means clustering method is introduced to select RBF centers based on the nominal IBVS path. Lyapunov analysis confirms the Uniformly Ultimately Bounded (UUB) stability. Simulation results demonstrate that the proposed method significantly reduces estimation errors and improves tracking accuracy compared to traditional schemes. Ultimately, this approach enhances the robustness and engineering practicality of robotic visual servoing through the deep coordination of control and estimation.

The tracking of moving object by implementing robot manipulator is one of the challenging task for many applications such as manufacturing, agriculture, logistics, healthcare, space, military, entertainment, etc. In the deployment of robotic manipulators with real-time object tracking for aforementioned important applications, the proper sensor surveillance and ensuring stability are major challenges. The purpose of this study is to design a precise and responsive object-tracking system by eliminating the complexities related to tedious mechanisms, rigidity, requirement of multiple sensors, etc. which are commonly associated with traditional systems. The robotic arms can be effectively designed to track moving objects autonomously with vision-based control. In comparison with different classical and traditional servoing approaches, the image-based visual servoing (IBVS) is more advantageous in vision-based control. The present article describes a new approach for IBVS-based tracking control of 2-degree-of-freedom (DOF) robotic arm by including object identification and trajectory tracking based crucial components. To solve the issues associated with IBVS, an accurate deep learning-based object detection framework is employed. The presented framework is utilized to detect and locate the objects in real-time. Further, an effective vision-based control technique is designed to control the 2-DOF robotic arm with the help of real-time response of object detection system. The validation of proposed control strategy is done by performing a simulation and experimental investigations with CoppeliaSim robot simulator and 2-DOF robotic arm, respectively. The findings reveal that the proposed deep learning controller for the vision-based 2-DOF robotic arm achieves good levels of accuracy and response time while performing visual servoing tasks. Furthermore, thorough discussion on possibility of using data-driven learning technique has been explored to improve the robustness and adaptability of the presented control scheme.

The robotic assembly of screws is the basic task for the automation assembly of complex equipment. However, a complete robotic assembly framework is difficult to be designed due to the integration of multiple technologies to achieve efficient and stable operations. In this paper, a robotic assembly workflow is proposed, which mainly consists of a feature extraction stage, a grasping stage, and an installation stage. In the feature extraction stage, a feature extraction algorithm consisting of a semantic segmentation network and an object classification module is designed. The semantic segmentation network segments the areas of multiple categories’ objects, and the object classification module selects an appropriate target object. The grasping stage and installation stage involve the position alignment of the objects. A position alignment method is developed based on image-based visual servoing using the point features extracted from the segmented areas. The experiments are conducted on a real robot. The alignment errors in grasping stage are less 0.53 mm. The assemblies for a M6-sized screw in ten experiments are successful. The experiment results verify the effectiveness of the proposed method.

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera’s field of view. A single monocular sensor’s sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system’s dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera’s field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera’s field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed.

Designing a robot with the best accuracy is always an attractive research direction in the robotics community. In order to create a Gough–Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough–Stewart platform: leg-direction-based visual servoing, line-based visual servoing, and image moment visual servoing. Depending on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough–Stewart platform for each type of controller. Then, we perform co-simulations on the three optimized Gough–Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps get both the optimum design parameters of the robot and the performance of the controller robot + dedicated controller.

This paper presents a novel image-based visual servoing (IBVS) controller for a six-degree-of-freedom (6-DoF) robot manipulator by employing a fuzzy adaptive model predictive control (FAMPC) approach. The control strategy allows the robot to track the desired feature points adaptively and fulfill kinematic constraints appearing in a vision-guided task with different initial Cartesian poses. To this aim, the successive linearization method is firstly employed to transform the nonlinear IBVS model to the linear time-invariant (LTI) one at each sampling instant. The nonlinear optimization problem is therefore degraded into a convex quadratic programming (QP) problem. Subsequently, a fuzzy logic is exploited to tune the weighting coefficients in the cost function on the basis of image pixels changes at each step, endowing the reliable adaptation capabilities to different working environments. Experimental comparison tests performed on a 6-DoF robot manipulator with an eye-in-hand configuration are provided to demonstrate the efficacy of the proposed controller.

This paper proposes an image-based visual servoing control method for a moving target of a quadrotor UAV (QUAV). Firstly, the dynamic image model with moving target parameters is established based on the image moment features in the virtual camera plane. For the unpredictability of the moving target in space, we use a high-order differentiator to estimate the state parameters of the moving target. In order to solve the problem of image depth information caused by a monocular camera, we derive a nonlinear finite-time linear velocity observer from the virtual image plane, which can not only estimate the linear velocity information of QUAV but also avoid the measurement of image depth. Based on the above information, we design the global finite-time controller and use Lyapunov theory to prove the finite-time stability of the system. Finally, the numerical simulations verify the convergence of the proposed control scheme, and the ROS gazebo simulations demonstrate the improved performance of the proposed control scheme in tracking error.