Ensuring both mobility and stability during colonoscopy is crucial for enhancing procedural efficiency and reducing the risk of complications. Traditional colonoscopy robots face challenges due to the fixed diameter of the colonoscope and the variability in colon anatomy. To address this, we propose a soft automatic anchoring system (SAAS) to enhance mobility and stability in colonoscopy robots. The SAAS features proximal and distal soft balloon anchors, employing origami principles to achieve a 42.2% higher maximum expansion capability compared to conventional flat surface anchors. With real-time pressure feedback, the SAAS automatically anchors upon contact detection, ensuring precise anchoring without overinflation while adapting to colon diameter variations and robot posture changes. Experimental results show a tenfold reduction in displacement during the stability test, significantly enhancing the robot’s performance under external loads. Performance comparison tests in phantom further demonstrated notable improvements in the efficiency of colonoscopy procedures using the SAAS. This system has the potential to greatly enhance the safety, precision, and overall efficiency of colonoscopy, offering substantial benefits for both medical practitioners and patient outcomes.

As one of the effective closed-loop control methods, visual servoing control methods are widely applied to continuum robots. However, existing visual servoing control methods mostly focus on accurate control of the robot’s end-effector, with less consideration given to the robot’s shape. In this work, a spatial position-based visual servoing obstacle-avoidable shape control framework for an 11-degree-of-freedom (DOF) hybrid continuum robot is proposed. In the control framework, a set of markers representing the shape of the continuum robot are measured and two spatial arcs are used to fit the shape. When controlling the redundant DOFs of the robot, position-based visual servoing shape control combined with obstacle avoidance is formulated as a quadratic programming problem, yielding the optimal solution at each sample time for the joint velocity vector of the 11-DOF hybrid continuum robot. Several experiments are conducted to validate the proposed control framework, which indicates the accuracy of the shape control achieves 0.88 mm.

This paper proposes discrete-time learning algorithms that utilize a data-driven technology to address the uncertain issues of optimization and structure. The main challenge lies in acquiring accurate optimization indices and Jacobian matrix, which can be addressed through iterative estimations enabled by these algorithms. On this basis, we propose a new model-adaptive kinematic control (MAKC) scheme for redundant manipulators without prior structure knowledge, incorporating the estimated optimization index and Jacobian matrix. To solve this scheme, a discretized data-driven neural dynamics (D3ND) controller is proposed based on the 94LVI algorithm, Kalman filter, and discrete-time learning algorithms. Theoretical analysis is provided to demonstrate its convergence. Subsequently, simulations and experiments are carried out on redundant manipulators using manipulability and joint drift as performance criteria. The results substantiate the robustness, practicability, and superiority of the proposed controller when encountering uncertain issues.

In recent years, many discrete neural dynamics models are presented based on continuous models to solve the multilinear tensor equation. However, these existing discrete models all depend on numerical algorithms, such as Euler difference formula and Taylor-type difference formula, which may suffer from the problem of fixed selections with limited feasible parameters. In this article, a few-shot-learning-like neural dynamics (FLLND) model is constructed to find the solution to the time-dependent multilinear tensor equation (TMTE), which opens a new road in constructing the discrete computing model from its continuous counterpart. Specifically, to keep the consistency and better generalization of the constructed model, a few-shot-learning-like method is leveraged to learn parameters from a small dataset. Then, theoretical analyses are conducted to demonstrate the convergence and robustness of the constructed FLLND model in solving the TMTE problem. Finally, several TMTE examples are provided to illustrate the effectiveness and practicality of the FLLND model.

There may be obstacles in the workspace of redundant manipulators, which generally pose a hidden danger to the safety execution. How to avoid obstacles reasonably is one of the goals of this article. To this end, a modified obstacle avoidance (MOA) method is proposed, which creates a larger feasible space for the escape velocity of redundant manipulators in a more concise form than the existing methods. Equipped with the MOA method, a trajectory-tracking and modified-obstacle-avoidance (TT-MOA) scheme for redundant manipulators is constructed. On the other hand, ubiquitous noises also influence the operation of redundant manipulators. Therefore, a noise-tolerant gradient neural dynamics (NTGND) model is proposed to tolerate noises when solving the TT-MOA scheme. Rigorous theoretical analyses prove the convergence and robustness of the NTGND model, and computer simulations and physical experiments demonstrate the practicability and superiority of the proposed methods compared with the existing techniques.

A novel binary channel fuzzy self-adjusted neural network (BCF-SANN) is proposed and researched for solving time-changing quadratic programming (QP) problems in this article. Unlike the fixed parameters of the typical zeroing neural network, the main parameters of the proposed BCF-SANN are time-changing, and its errors are adaptively quickly convergent. The biggest advantage of the novel neural network is that it combines a fuzzy self-adjusted controller, which takes the errors and derivatives of errors as fuzzy inputs and neural networks, further improving the convergence and robustness of the neural networks. To design the novel neural network, a time-changing QP problem is first established; then, using Lagrange's law, the time-changing QP problem is transformed into a time-changing matrix equation; and finally, based on the time-changing parameter neural dynamics method, a novel BCF-SANN is proposed. The detailed design process is given in this article, and the convergence and robustness of the proposed BCF-SANN are proved by theoretical analysis. Through comparative experiments, it is demonstrated that the proposed BCF-SANN has a faster convergence rate and stronger robustness than the traditional zeroing neural network and 1-D fuzzy recurrent neural network (RNN).

Accurate rigid-body dynamics is crucial for serial industrial robot applications such as force control and physical human-robot interaction. Despite decades of research, the precise identification of dynamic parameters—particularly low-magnitude inertia parameters—remains a challenge for serial industrial robots. Researchers usually focus on developing various parameter estimation methods, while optimizing exciting trajectories in similar ways, typically minimizing the condition number of the information matrix. However, such optimization usually fails to ensure sufficient excitation for each parameter, due to non-convex coupling effects. To address this limitation, we propose a fully decoupled rigid-body dynamics identification (FDRDI) method in this article. This approach innovatively eliminates coupling effects by using novel symmetrical exciting trajectories based on reciprocating S-curve (RSC). This innovation enables the independent identification of dynamic parameters associated with joint friction, as well as the gravity and inertia of links and payloads. Comparative experiments show that FDRDI achieves superior identification accuracy, evidenced by reduced joint torque prediction errors and payload parameter estimation errors.

Existing computational models for addressing time-dependent quadratic programming (TDQP) problems encounter some challenges, such as generating lagging errors, lack of noise immunity, and inability to be distributed. To handle these challenges, this article proposes a distribution-allowed noise-resistant neural dynamics (DANRND) model to solve TDQP problems with equality and inequality constraints by introducing auxiliary variables rather than by using the nonlinear complementary problem (NCP) function. The proposed model is able to effectively eliminate the hysteresis error and suppress the influence of noises. Specifically, the proposed model is capable of implementation in a distributed manner, which extends its scope of applications. Then, theoretical analyses are provided to prove the global convergence in both noise-free and noisy conditions. Simulative examples and comparison results with existing methods are offered, demonstrating the superiority of the proposed DANRND model. Finally, a distributed cooperative task based on the k-winner-take-all (kWTA) operation is performed on a multirobot platform to further verify the distributed implementation of the proposed DANRND model.

Flexible endoscopes are commonly adopted in narrow and confined anatomical cavities due to their higher reachability and dexterity. However, prolonged and unintuitive manipulation of these endoscopes leads to an increased workload on surgeons and risks of collision. To address these challenges, this paper proposes a CT-guided control framework for the diagnosis of maxillary sinusitis by using a robotic flexible endoscope. In the CT-guided control framework, a feasible path to the target position in the maxillary sinus cavity for the robotic flexible endoscope is designed. Besides, an optimal control scheme is proposed to autonomously control the robotic flexible endoscope to follow the feasible path. This greatly improves the efficiency and reduces the workload for surgeons. Several experiments were conducted based on a widely utilized sinus phantom, and the results showed that the robotic flexible endoscope can accurately and autonomously follow the feasible path and reach the target position in the maxillary sinus cavity. The results also verified the feasibility of the CT-guided control framework, which contributes an effective approach to early diagnosis of sinusitis in the future.

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.

Parallel robots including Gough–Stewart platforms are widely applied in industrial factories and medical fields. This article investigates a kinematically redundant Gough–Stewart robot, designs, and compares three novel zeroing neural networks (ZNNs) to serve as the redundancy-resolution controllers. Unlike the existing ZNN controllers, the newly designed ZNN controllers are endowed with the capability to handle joint constraints by using a nonlinear complementarity problem function without introducing any extra hyperparameters, guaranteeing the safety of the robot. The proposed ZNN controllers are training-free, noniterative, and more accurate, as compared with other typical neural controllers for kinematic control of the Gough–Stewart robot. Theoretically, the convergence analyses of the ZNN controllers are rigorously carried out. Corresponding discrete neural controllers are established and then applied to the kinematically redundant Gough–Stewart robot with two path-tracking tasks exemplified. The path-tracking results comparatively substantiate the effectiveness and superiority of the ZNN controllers for redundancy resolution under joint constraints.

Remote center of motion (RCM) has become a rising research direction in the field of robotics. It means that a robot with a rod-shaped end-effector operates through a tiny hole on the surface. Thereinto, a key issue is the deviation of the RCM point's position, apart from the operating accuracy. In addition, considering that an RCM robotic system generally consists of a commercial robot and a specialized rod-shaped end-effector, there exist some errors in the structural information related to the attached end-effector. In this article, a remote center of cyclic motion scheme with a data-driven technology is proposed to control robots, of which end-effectors' structural parameters are inaccurate. Meanwhile, a recurrent neural network is proposed to figure out the scheme's solution, with the relevant theoretical analysis given. Furthermore, simulative and physical experiments on a FRANKA Panda robot with a rod-shaped end-effector are conducted to validate the control scheme's effectiveness distinctly.

Designing distributed control systems (DCS) is challenging because we need to balance the application control performance with the real-time hardware resource costs. Compared to the abundance of automated tools for monolithic systems, managing the design and implementation of DCS still heavily relies on human expertise. To address this challenge, we propose a human-augmenting design tool that suggests and explores tradeoffs in co-optimizing controller simplification and multiprocessor distributed task scheduling with a simple dial. Our work creatively combines sparsity-based optimal controller simplification with task consolidation, to achieve an optimal control performance on the smallest distributed hardware footprint. Our unique task consolidator adapts to various real-time scheduling algorithms and allocates control tasks to the fewest nodes under the utilization bounds of the chosen real-time scheduler. To account for real-time guarantees when assigning the controller, we introduce an improved worst-case execution time (WCET) model based on a mixed Weibull distribution. This new WCET model provides state-of-the-art accuracy of WCET based upon fewer data samples, thus reducing design-time effort and improving run-time performance. With our tool, designers can virtually simulate the theoretical optimum, physically download it, or experimentally explore alternative tradeoffs between system cost and controller performance. In a real-world implementation, our tool reduced 60% of hardware costs by trading off merely 0.14% of control performance. By automating the “simplification – demux – WCET estimation – task consolidation” pipeline, our tool allows control engineers to balance control performance and system cost directly, speeding up manual distributed controller design by 16 times.

This article focuses on the state feedback control design problem for a specific class of strict-feedback fractional-order nonlinear systems with unknown state time-varying delays, external disturbance, and limited observability. A state feedback control scheme is proposed based on the introduction of a reduced-order observer. The design of this control scheme utilizes Lyapunov-Krasovskii functional with static gain, leading to the development of a novel delay-independent and memoryless control strategy. The main objective is to guarantee the boundedness of all signals in the closed-loop system and ensure asymptotic stability. The effectiveness of the proposed control scheme is validated through simulation and experimental tests, demonstrating their capability to address the control challenges posed by the considered system models in neural networks, robot control and a real-time oscillator applications. Simulation and empirical results illustrate the improved performance achieved by the control strategy over two existing adaptive control schemes in terms of lower-steady-state error, faster system responsiveness and increased stability (milder oscillation) in the state responses. The proposed control scheme offers a practical way of achieving desired control objectives, providing valuable insights for researchers and practitioners in the field of control engineering.

In robotic deformable object manipulation (DOM) applications, constraints arise commonly from environments and task-specific requirements. Enabling DOM with constraints is, therefore, crucial for its deployment in practice. However, dealing with constraints turns out to be challenging due to many inherent factors, such as inaccessible deformation models of deformable objects (DOs) and varying environmental setups. This article presents a systematic manipulation framework for DOM subject to constraints by proposing a novel path set planning and tracking scheme. First, constrained DOM tasks are formulated into a versatile optimization formalism, which enables dynamic constraint imposition. Because of the lack of the local optimization objective and high state dimensionality, the formulated problem is not analytically solvable. To address this, planning of the path set, which collects paths of DO feedback points, is proposed subsequently to offer feasible path and motion references for the DO in constrained setups. Both theoretical analyses and computationally efficient algorithmic implementation of path set planning are discussed. Lastly, a control architecture combining path set tracking and constraint handling is designed for task execution. The effectiveness of our methods is validated in a variety of DOM tasks with constrained experimental settings.

Existing robotic endoscopes for laparoscopic surgery, predominantly rigid or limited in dexterity, occupy a large motion space1, The large occupied motion space necessitates large incisions and reduces the motion space for surgeons to simultaneously operate other surgical instruments. Meanwhile, surgeons only have limited view adjustment capability to avoid occlusion and they often have to lift/push some organs to observe occluded target lesions in some operations such as cholecystectomy. The situation gets worse when the operations are on obese patients. In this paper, we develop a novel dexterous robotic flexible endoscope (DRFE), which is comprised of a concentric cable-driven structure and a 2-DoF articulated joint attached to the end of DRFE, for laparoscopic surgery. The proposed design occupies much less motion space both inside and outside human body as compared to conventional robotic flexible endoscopes. When used in surgery, the part of the endoscope outside the body can remain still, which reduces the risk of expanding the incision and simplifies the structure of the remote center mechanism. Simulation and experimental studies are performed to validate the effectiveness of the proposed device in the improvement of vision occlusion and usability. Initial results reveal that the DRFE is highly dexterous and accurate in observing lesions with vision occlusion.

Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand multimodal perception for effective and precise task execution. However, these present a novel challenge to robots as they lack the ability to combine these multimodal stimuli for performing contact-rich tasks. Learning-based methods have attempted to model multi-modal contact-rich tasks, but they often require extensive training examples and task-specific reward functions which limits their practicality and scope. Hence, we propose a generalizable model-free learning-from-demonstration framework for robots to learn contact-rich skills without explicit reward engineering. We present a novel multi-modal sensor data representation which improves the learning performance for contact-rich skills. We performed training and experiments using the real-life Sawyer robot for three everyday contact-rich skills – cleaning, writing, and peeling. Notably, the framework achieves a success rate of 100% for the peeling and writing skill, and 80% for the cleaning skill. Hence, this skill learning framework can be extended for learning other physical manipulation skills.

The beneficial aspects of fully-actuated multirotor UAVs, provided by nonparallel rotor configuration, are increasingly being recognized and utilized to great benefit in high-precision applications. Full six-degree-of-freedom force control, higher control bandwidth and improved disturbance rejection prove valuable. However, the cant angle will cause great multirotor dihedral effect and significantly affects blade flapping, which decreases the flight performance of nonparallel actuated UAVs. Therefore, this paper presents a novel aerodynamic model for fully-actuated hexrotor UAVs while considering the aerodynamic effects caused due to tilt angled propeller configurations. In the proposed aerodynamic model, the significance of multirotor dihedral effect, defined as an aerodynamic coefficient proportional to the relative linear velocity of the UAV, is modeled for nonparallel actuators. Additionally, the modeling for blade flapping effect for cant angled propellers is provided to accurately model the aerodynamics. Wind tunnel experiments were conducted to characterize the aerodynamic constants for multirotor dihedral effect, blade flapping effect and air friction. Experimental results are presented to validate the proposed aerodynamic model on a fully-actuated hexrotor UAV (Purdue’s Dexterous Hexrotor). Lastly, the multirotor dihedral effect and blade flapping effect at different cant angles and at different wind speeds are analyzed.

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.

Inspection for structural properties (surface stiffness and coefficient of restitution) is crucial for understanding and performing aerial manipulations in unknown environments, with little to no prior knowledge on their state. Inspection-on-the-fly is the uncanny ability of humans to infer states during manipulation, reducing the necessity to perform inspection and manipulation separately. This paper presents an infrastructure for inspection-on-the-fly method for aerial manipulators using hybrid physical interaction control. With the proposed method, structural properties (surface stiffness and coefficient of restitution) can be estimated during physical interactions. A three-stage hybrid physical interaction control paradigm is presented to robustly approach, acquire and impart a desired force signature onto a surface. This is achieved by combining a hybrid force/motion controller with a model-based feed-forward impact control as intermediate phase. The proposed controller ensures a steady transition from unconstrained motion control to constrained force control, while reducing the lag associated with the force control phase. And an underlying Operational Space dynamic configuration manager permits complex, redundant vehicle/arm combinations. Experiments were carried out in a mock-up of a Dept. of Energy exhaust shaft, to show the effectiveness of the inspection-on-the-fly method to determine the structural properties of the target surface and the performance of the hybrid physical interaction controller in reducing the lag associated with force control phase.

A flexible endoscope introduces more dexterity to the image capturing in endoscopic surgery. However, manual control or automatic control based on instrument tracking does not handle the misorientation between the endoscopic video and the surgeon. We propose an automatic flexible endoscope control method that tracks the surgeon's head with respect to the object in the surgical scene. The robotic flexible endoscope is actuated so that it captures the surgical scene from the same perspective as the surgeon. The surgeon wears a head-mounted display to observe the endoscopic video. The frustum of the flexible endoscope is rendered as an augmented reality overlay to provide surgical guidance. We developed the prototype, FlexiVision, integrating a 6-DOF robotic flexible endoscope based on the da Vinci Research Kit and Microsoft HoloLens. We evaluated the proposed automatic control method via a lesion observation task, and evaluated the AR surgical guidance in a lesion targeting task. The multi-user study results demonstrated that, for both tasks, FlexiVision significantly reduced the completion time (by 59% and 58%), number of errors (by 75% and 95%) and subjective task load level. With FlexiVision, the flexible endoscope could act as the surgeon's eyes teleported into the abdominal cavity of the patient.