Aiming at time-variant problems solving, a special type of recurrent neural networks, termed zeroing neural network (ZNN), has been proposed, developed, and validated since 2001. Although equality-constrained time-variant quadratic programming (TVQP) has been well solved using the ZNN approach, TVQP problems with inequality constraints involved have not been satisfactorily handled by the existing ZNN models. To overcome this issue, this paper designs a ZNN model with exponential convergence for solving equality- and inequality-constrained TVQP problems. Considering a fast convergence is preferred in some time-critical applications in practice, a predefined-time stabilizer is for the first time utilized to endow the ZNN model with predefined-time convergence, leading to a predefined-time convergent ZNN (PTCZNN) model that exhibits an antecedently- and explicitly-defined convergence time. Theoretical analysis is performed with the convergence of the two ZNN models including the predefined-time convergence of the PTCZNN model rigorously proved. Validations are comparatively conducted to verify the effectiveness and superiority of the PTCZNN model in terms of convergence performance. To demonstrate the potential applications, the PTCZNN model is applied to image fusion and kinematic control of two robotic arms with joint limits considered. The efficacy and applicability of the PTCZNN model are validated by the illustrative examples. This is the first time to develop a ZNN model working as a quadratic programming solver that is applicable to kinematic control of robotic arms with joint constraints handled since the emergence of ZNNs.

Automation in robotic surgery has become an increasingly attractive topic. Though full automation remains fictional, task autonomy and conditional autonomy are highly achievable. Apart from the performance of task fulfillment, one major concern in robotic surgery is safety. In this paper, we present a flexible endoscope that can help to guide the minimally invasive surgical operations automatically. It is developed based on the tendon-driven continuum mechanism and is integrated with the da Vinci Research Kit (DVRK). In total, the proposed flexible endoscope has six degree-of-freedoms (DOFs). Visual servoing is adopted to automatically track the surgical instruments. During the tracking, optimal control method is used to minimize the motion and space occupation of the flexible endoscope, which will improve the safety of both the robot system and the assistants nearby. Comparing with the existing rigid endoscope, both the experimental results and the user study results show that the proposed flexible endoscope has advantages of being safer and less space occupation without reducing its comfort level.

Nowadays, there are time-critical applications involving linear equations, such as the fault reconstruction problem, where hard response time constraints and robustness to external disturbances are expected. Zeroing neural network (ZNN) is one of the effective solutions to time-variant problems including time-variant linear equations. This paper proposes a strictly predefined-time convergent and noise-tolerant ZNN (SPTC-NT-ZNN) to solve time-variant linear equations. Differing from the existing ZNN models, the designed SPTC-NT-ZNN is enhanced to be convergent in strictly predefined time while exhibiting noise tolerance. This guarantees the desirable timely convergence and robustness for time-critical applications. In theory, the strictly predefined-time convergence and noise-tolerance properties of the proposed SPTC-NT-ZNN are mathematically proved in a rigorous manner. Comparative validations are performed to verify that the SPTC-NT-ZNN outperforms existing typical ZNNs, regarding the convergence and robustness performance. To demonstrate potential applications, the SPTC-NT-ZNN is applied to 3D stereo reconstruction and motion control of a Franka Emika Panda robot, showing the efficacy of the proposed method.

Existing continuum robots still lack the necessary dexterity and manipulability to inspect and operate in confined space with multiple obstacles. In this article, we propose a novel hybrid continuum robot (HCR) with enhanced dexterity and manipulability. The proposed nine-degree-of-freedom robot can fit in confined space with multiple obstacles by changing the length of its inner hybrid-structure section and outer flexible section. A rotatable inner hybrid-structure section and an additional distal wrist enable rotation motion and sharp bending at the distal end of the HCR, which enhances its dexterity and manipulability. Besides, we develop a detachable, lightweight (2.5 kg), and compact (41 cm×15 cm×6 cm) actuation system for the HCR. It can be easily integrated with existing commercial robot arms to adjust the pose of the HCR's base, which further enlarges its workspace. In order to demonstrate the HCR's obstacle avoidance capability in confined space, a path-planning method is proposed. Simulation results show the dexterity and manipulability of the HCR enhanced by 98% and 3804 times compared with an existing concentric flexible robot in a confined space with one obstacle. Experiments validate the feasibility of the HCR and its path-planning method.

Redundant manipulators, as mechanical equipments imitating human arms, have been applied to various areas in recent years from the perspective of control. Different from pure control technologies, the motion capability of a human arm is achieved by a complex and efficient neural system, with the cerebellum playing a pivotal role. Motivated by this fact, we design a cerebellum model based on an echo state network (ESN) for the learning and control of redundant manipulators. In addition, to simulate the skillful control ability of the cerebellum over movements of human arms, the proposed model is constructed at the joint velocity level. Furthermore, to improve the accuracy and applicability, we propose an ESN-based Kalman-filter-incorporated and cerebellum-inspired (KFICI) scheme for the learning and control of redundant manipulators with Kalman filter incorporated. The proposed scheme enables a redundant manipulator to track the desired trajectory at the velocity level and tolerate noises. Finally, simulations and experiments based on a physical redundant manipulator are performed to verify the effectiveness of the proposed control scheme.

Endoscopes play an important role in minimally invasive surgery (MIS). Due to the advantages of less occupied motion space and enhanced safety, flexible endoscopes are drawing more and more attention. However, the structure of the flexible section makes it difficult for surgeons to manually rotate and guide the view of endoscopes. To solve these problems, we developed a 6-DOF robotic stereo flexible endoscope (RSFE) based on the da Vinci Research Kit (dVRK). Then an image-based endoscope guidance method with depth information is proposed for the RSFE. With this method, the view and insertion depth of the RSFE can be adjusted by tracking the surgical instruments automatically. Additionally, an image-based view rotation control method is proposed, with which the rotation of the view can be controlled by tracking two surgical instruments. The experimental results show that the proposed methods control the direction and rotation of the view of the flexible endoscope faster than the manual control method. Lastly, an ex vivo experiment is performed to demonstrate the feasibility of the proposed control method and system.