For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few works of literature are concerned with the convergence speed. In this paper, we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to the traditional methods, the information of parameter estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, the Newton-Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition-based model reduction method was designed to remedy the potential singularity problems of the NE regressor. Moreover, an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time (FT) identification algorithm based on a 7-DOF arm of Baxter robot.

Although great progress has been made in generic object detection by advanced deep learning techniques, detecting small objects from images is still a difficult and challenging problem in the field of computer vision due to the limited size, less appearance, and geometry cues, and the lack of large-scale datasets of small targets. Improving the performance of small object detection has a wider significance in many real-world applications, such as self-driving cars, unmanned aerial vehicles, and robotics. In this article, the first-ever survey of recent studies in deep learning-based small object detection is presented. Our review begins with a brief introduction of the four pillars for small object detection, including multiscale representation, contextual information, super-resolution, and region-proposal . Then, the collection of state-of-the-art datasets for small object detection is listed. The performance of different methods on these datasets is reported later. Moreover, the state-of-the-art small object detection networks are investigated along with a special focus on the differences and modifications to improve the detection performance comparing to generic object detection architectures. Finally, several promising directions and tasks for future work in small object detection are provided. Researchers can track up-to-date studies on this webpage available at: https://github.com/tjtum-chenlab/SmallObjectDetectionList .

This paper proposes an enhanced robot skill learning system considering both motion generation and trajectory tracking. During robot learning demonstrations, dynamic movement primitives (DMPs) are used to model robotic motion. Each DMP consists of a set of dynamic systems that enhances the stability of the generated motion toward the goal. A Gaussian mixture model and Gaussian mixture regression are integrated to improve the learning performance of the DMP, such that more features of the skill can be extracted from multiple demonstrations. The motion generated from the learned model can be scaled in space and time. Besides, a neural-network-based controller is designed for the robot to track the trajectories generated from the motion model. In this controller, a radial basis function neural network is used to compensate for the effect caused by the dynamic environments. The experiments have been performed using a Baxter robot and the results have confirmed the validity of the proposed methods.

Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces generated by grasped objects, the dual-arm robot control with uncertain kinematics and dynamics raises a challenging problem. In this paper, an adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object. Also, a novel finite-time convergence parameter adaptation technique is developed for the estimation of kinematic parameters and fuzzy logic weights, such that the estimation can be guaranteed to converge to small neighborhoods around their ideal values in a finite time. Moreover, a partial persistent excitation property of the Gaussian-membership-based fuzzy basis function was established to relax the conventional persistent excitation condition. This enables a designer to reuse these learned weight values in the future without relearning. Extensive simulation studies have been carried out using a dual-arm robot to illustrate the effectiveness of the proposed approach.

In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform.

In this paper, a physical human-robot interaction approach is presented for the developed robotic exoskeleton using admittance control to deal with a human subject's intention as well as the unknown inertia masses and moments in the robotic dynamics. The human subject's intention is represented by the reference trajectory when the robotic exoskeleton is complying with the external interaction force. Online estimation of the stiffness is employed to deal with the variable impedance property of the robotic exoskeleton. Admittance control is first presented based on the measured force in order to generate a reference trajectory in interaction tasks. Then, adaptive control is proposed to deal with the uncertain robotic dynamics and a stability criterion can be obtained. Bounded errors are shown in the motion tracking while the robustness of the variable stiffness control is guaranteed. The experimental results indicate that the proposed control enables the human subjects to execute an admittance control task on the exoskeleton robot effectively.