Inspired by soft creatures and structures in nature, continuum robots exhibit remarkable flexibility, safe interaction, and ease of miniaturization, showcasing vast application potential. However, their flexible structure renders analytical methods inadequate for precise modeling and control, while existing data-driven approaches suffer from low data efficiency and unproven theoretical control performance. This paper aims to achieve data-efficient modeling and reliable control of continuum robots through innovative algorithms, exploring the performance of the new method from both theoretical and experimental perspectives. Specifically, we utilize neural ordinary differential equations (NODE) to achieve data-efficient modeling of continuum robots and investigate the performance of the modeling method. Then, we propose a novel predefined-time-synchronized stable zeroing neurodynamics (PTSS-ZND) model. By combining the NODE method and the PTSS-ZND method, we propose a reliable data-driven control system. Through rigorous theoretical analysis, we prove the stability and predefined-time convergence of the data-driven control system. Finally, through simulations and physical experiments, we validate the feasibility and convergence of the novel method and its advantages over existing data-driven methods. Experiments on one- and three-segment continuum robots indicate that the proposed method achieves a root mean square position error (e.g., 2.5 mm for the three-segment robot) of less than 1% of the robot length using fewer than 100 data samples. Our method also demonstrates robust performance under various external and internal disturbances. In addition, it can potentially be extended for end-effector pose control.

Traditional data-driven control methods often require large amounts of training data, posing significant challenges for continuum robots. Recently, neural ordinary differential equation (NODE) methods have demonstrated impressive capabilities for data-efficient modeling of continuum robots. However, existing NODE-based control methods still face limitations in terms of convergence and robustness. In this paper, we propose a data-driven iterative learning control system for continuum robots, leveraging NODE for modeling. Within this framework, by incorporating online parameter learning, the proposed control system continuously adapts to various uncertainties associated with continuum robots, resulting in improved convergence and robustness in repetitive tasks. The effectiveness of the proposed method is validated through simulations and physical experiments, and comparative analysis highlights its superior accuracy over existing approaches.

Robot manipulator path tracking, recognized as a crucial aspect in robot manipulator control, has garnered significant attention from researchers. In this paper, to address the path tracking problem of robot manipulators with unknown models, a novel Jacobian pseudoinverse estimator is first proposed based on Zhang neurodynamics method. The estimator directly provides an efficient and accurate estimation of the Jacobian matrix pseudoinverse, avoiding the complicated operation of matrix pseudoinverse and preventing potential singularity phenomenon of the Jacobian matrix. By utilizing the Euler difference formulas, a discrete model-free and Jacobian-pseudoinverse-free Zhang neurodynamics algorithm is proposed. The proposed algorithm focuses on leveraging the available current and previous known information to predict the future unknown information. Detailed theoretical analyses and proofs ensure the convergence and stability of the proposed algorithm. Finally, comparative experiments with various effective model-free algorithms, and experimental validations on different types of robot manipulators (UR5, Franka Emika Panda, and Kinova Gen3 robot manipulators) using various experimental platforms (MATLAB, CoppeliaSim, and physical platforms) illustrate the effectiveness of the proposed algorithm. Note to Practitioners—This paper is motivated by addressing the prevalent challenge of unknown models in real-time path tracking for robot manipulators. In this paper, a novel discrete model-free and Jacobian-pseudoinverse-free Zhang neurodynamics algorithm is proposed. Different from the existing model-free algorithms, the proposed algorithm avoids the complicated operation of computing the pseudoinverse of matrix without compromising precision, significantly reducing the computational complexity and preventing potential singularity phenomenon of the Jacobian matrix. The average computation time per updating for the proposed algorithm is approximately$0.1\,{\mathrm { ms}}$, which is significantly less than the sampling gap of the operation. This allows it to effectively meet the real-time requirements for robot manipulator path tracking. In addition, the error of the proposed algorithm is approximately$2\,\mu {\mathrm { m}}$, which can meet the requirements of most practical application scenarios. Moreover, the accuracy of the algorithm is limited by the differential formula and sampling gap. Improving the accuracy and robustness of the algorithm by using more accurate difference formulas and filtering technique will be our future research direction.

The reconfigurability and structural complexity of robotic manipulators introduce significant challenges to their kinematic modeling, making traditional model-based methods less feasible. This article studies the synchronized tracking control problem of a dual-arm system with completely unknown kinematic model for the first time. A gradient neurodynamic method is presented to learn the unknown Jacobian matrices of dual manipulators. Then, we innovatively employ the coupled error in dual quaternion form as the state function of the zeroing neurodynamic model to control position and orientation simultaneously, leading to a more streamlined and compact inverse kinematics solution. Furthermore, by transforming task-space coupled errors into joint-space errors, we improve the cerebellar neural network to a mode compatible with kinematic control and, for the first time, utilize it to optimize synchronized kinematic control. Finally, the effectiveness of the proposed method is validated by simulations and experiments on various robotic manipulators.

Redundant manipulators have shown great potential in the application of robots. These manipulators possess additional degrees of freedom beyond what is essential for completing specific tasks, presenting an opportunity to optimize energy usage. However, the existence of additional degrees of freedom also brings control challenges. Due to the ability to address problems of time-varying tracking, zeroing neural network (ZNN) is gradually widely used in the control of redundant manipulators. Discrete models are often used in engineering, and the sampling gap selected during discretization is an important factor that affects the tracking precision. Large sampling gaps require less computational consumption but yield lower tracking precision, whereas small sampling gaps result in higher precision but at a greater computational cost. In this article, a minimum energy consumption scheme (MECS) for the time-varying tracking control task of redundant manipulators is presented first. By applying the ZNN design formula, the continuous ZNN (CZNN) model is established to solve the MECS. Subsequently, Euler discretization formula is utilized to transform the CZNN model into its discrete form, known as the discrete ZNN (DZNN) model. Then a dual-input–single-output fuzzy control system is designed to obtain suitable sampling gaps. The fuzzy-control-aided ZNN (FCAZNN) model enables redundant manipulators to track desired paths with the expected precision. Finally, a series of experiments are carried out in this article to demonstrate the advantages of FCAZNN model, including both computer simulations and physical experiments.

The robot industry is developing rapidly, and how to control the redundant manipulators precisely and effectively has become a new hot topic in industry’s development. In recent years, many scholars in the industry have also proposed various control methods. However, most of these methods are proposed assuming that the Jacobian matrix is known. Actually, in practical applications, the detailed information of Jacobian matrix is often not precisely known. Therefore, this article develops a novel data-driven recurrent neural network (RNN) model that can update the Jacobian matrix and joint angles. By defining two dynamic error functions, two RNN designed formulas are used to obtain a continuous RNN (CRNN) model. Subsequently, the CRNN model is discretized by using Euler forward formula, and a discrete RNN (DRNN) model is generated. Then, a classic sliding mode control (SMC) algorithm is introduced, and DRNN-SMC model is further proposed. Moreover, the corresponding rigorous mathematical derivation and proof are carried out. In addition, simulation tests are carried out by using the Kinova Gen2 manipulator, comparing the DRNN model and PD controller, as well as the DRNN-SMC model and DRNN model, validating the precision of the DRNN-SMC model. Additionally, practical experiments using the Kinova Gen3 manipulator are performed to showcase the applicability and versatility of the DRNN-SMC model.

Neural networks have been widely investigated for the control of robot manipulators and recurrent neural network (RNN) is accepted as a powerful tool for visual servoing. Different from existing control schemes for robot-camera systems, this article proposes a novel image-based visual servoing (IBVS) control scheme for both the regulation and tracking control of robot manipulators in the framework of a special class of RNN, termed zeroing neural network (ZNN), which does not require prior knowledge about camera configuration and kinematic model parameters. The proposed control scheme is composed of a data-driven mapping estimator and a controller, both of which are designed based on ZNN. To facilitate the deployment of the proposed IBVS control scheme, a discrete-time version of the proposed control scheme is developed. Theoretical analysis for the proposed method is presented in terms of convergence, stability, and robustness. In addition, simulations and experiments are carried out based on different types of robot-camera systems to verify the efficacy and portability of the proposed control scheme for solving regulation and trajectory IBVS problems. Moreover, comparative studies are performed to reveal the merits of the proposed control scheme.

Different from the common static and continuous-time dynamic problems of unconstrained/constrained nonlinear optimization, this article aims to investigate a discrete-time dynamic problem of nonlinear optimization with multiple types of constraints, which can be succinctly termed as future multiconstrained nonlinear optimization (FMCNO) problem because of the unknown future. Considering the unique advantages of neural networks with parallelism and fuzzy control systems (FCSs) with adaptivity, a fuzzy-enhanced robust discretized zeroing neural network (FER-DZNN) model is proposed to address the FMCNO problem. Specifically, by introducing a fuzzy factor outputted from an FCS with dual inputs, the FER-DZNN model is designed on the basis of an FER evolution rule and a five-step look-ahead discretization rule. Moreover, theoretical results are provided to indicate the convergence and robustness of the FER-DZNN model under various noises. Finally, two illustrative examples, including an application example to robotic manipulator control, are presented to substantiate the superior convergent and robust performance of the FER-DZNN model under various noises for addressing the FMCNO problem.

As the zeroing neural network (ZNN) approach needs human intervention to handle temporally variant quadratic program (TVQP) problem, which results in less flexibility and convenience, especially considering the situation of tolerated precision given. Aiming at these weaknesses, in this article, we propose a type of adaptive ZNN (AZNN) approach on solving TVQP problem. Specifically, we design and propose a novel continuous-time AZNN model and two adaptive time-discretization techniques with different precision to obtain two discrete-time AZNN (DAZNN) solvers, making them more readily implemented on digital computers. The convergence analyses for these model and solvers are theoretically proved. Comparative experiments among the proposed DAZNN solvers and four existing solvers are conducted to verify the efficacy and superiority of the DAZNN solvers. Furthermore, simulative and physical experiments for the position tracking of JACO2 mechanical arm are successfully performed. Finally, based on the AZNN approaches, a novel adaptive kinematics planner and two adaptive solvers are proposed and applied for the pose tracking of the Franka Emika Panda mechanical arm, further substantiating their efficacy and effectiveness.

Heterogeneous continuum robots (HCRs) with different structures have been designed for different purposes, whereas the coordination of multiple HCRs has received little attention. On one hand, multiple HCRs coordination brings the possibility of performing complicated tasks. On the other hand, the structural diversity of HCRs poses great difficulties to their modeling and control. This article proposes a model-free scheme for the synchronous motion control of multiple HCRs. The control problem is formulated as two convex optimization problems in a model-free closed-loop framework, including control quantity estimation and Jacobian matrix estimation. The proposed approach aims at addressing the synchronous motion problem of multiple HCRs in a decentralized way. The design of model-free feedback control guarantees the high adaptability of the proposed method for a wide range of HCRs. Simulation studies are performed to verify the effectiveness and adaptability of the proposed scheme for multiple HCRs. Comparative studies verify that the tracking error synthesized by the proposed method is about two-thirds lower than that of the existing method while the computational cost is similar, which reveals the merit of the proposed method in terms of accuracy. Finally, the feasibility and effectiveness of the proposed method are also verified by hardware-in-loop simulations and physical experiments on the synchronous motion control of a cable-driven continuum robot and a concentric-tube robot.

This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefined-time convergence property guarantees that the regulation task can be finished by robotic manipulators in a preset time, in spite of the initial state of manipulators. This feature will facilitate the scheduling of a series of tasks in industrial applications. To this end, a varying-parameter predefined-time convergent zeroing neural dynamics (ZND) model is first proposed and employed to solve the regulation problem. As well as the primary task, a conventional ZND model is utilized to achieve the avoidance of obstacle. The stability of the proposed controller is analyzed based on the Lyapunov stability theory. For the sake of dealing with the unknown kinematic model of robotic manipulators, gradient neural dynamics (GND) models are exploited to adapt the Jacobian matrices just relying on the control signal and sensory output, which enables us to control robotic manipulators in a model-free manner. Finally, the efficacy and merits of the proposed control method are verified by simulations and experiments, including a comparison with the existing method.

The pursuit-evasion game is a critical problem in artificial intelligence and draws a lot of attentions. In this article, we study the coordinated capture of multiple targets using multiple pursuers. A task allocation algorithm named distributed multitarget k-winners-take-all (DMK-WTA) is proposed for multiple evaders and multiple pursuers in this article, which is distributed and based on competition. In this algorithm, pursuers obtain hunting qualification through competition of task cost. After that, robots are controlled by predator-pack encirclement model (PPM), through which pursuers can automatically navigate to the target while avoiding collisions with obstacles and other robots. Combined with DMK-WTA and PPM, a distributed multitarget pursuit scheme in a dynamic environment has formed. By comparing with Kuhn-Munkres algorithm and genetic algorithm, we have evaluated the efficiency of DMK-WTA algorithm. Extensive simulations and physical experiments are conducted on a variety of robots to verify the viability and applicability of the proposed approach.

In this article, being different from conventional time-discretization (simply called discretization) formulas, explicit linear left-and-right 5-step (ELLR5S) formulas with sixth-order precision are proposed. The general sixth-order ELLR5S formula with four variable parameters is developed first, and constraints of these four parameters are displayed to guarantee the zero stability, consistence, and convergence of the formula. Then, by choosing specific parameter values within constraints, eight specific sixth-order ELLR5S formulas are developed. The general sixth-order ELLR5S formula is further utilized to generate discrete zeroing neural network (DZNN) models for solving time-varying linear and nonlinear systems. For comparison, three conventional discretization formulas are also utilized. Theoretical analyses are presented to show the performance of ELLR5S formulas and DZNN models. Furthermore, abundant experiments, including three practical applications, that is, angle-of-arrival (AoA) localization and two redundant manipulators (PUMA560 manipulator and Kinova manipulator) control, are conducted. The synthesized results substantiate the efficacy and superiority of sixth-order ELLR5S formulas as well as the corresponding DZNN models.

Optimization problems are frequently encountered in various fields. In this article, the unconstrained time-variant convex optimization (UTVCO) problem is investigated. Generally, gradient neural network (GNN) is a traditional and effective method for solving time-invariant problems by making use of the gradient information. However, GNN is less effective on time-variant problems. On the other hand, Zhang neural network (ZNN) performs well on time-variant problems by exploiting the time-derivative information. In order to solve the UTVCO problem effectively and quickly with the help of the gradient information, inspired by the two methods, gradient-feedback ZNN (GZNN) is presented by taking both advantages of GNN and ZNN to solve the UTVCO problem. The main contributions are presented as follows. 1) The GZNN model for solving the UTVCO problem is proposed and analyzed theoretically. 2) Comparisons among GZNN, ZNN, GNN, and other models are presented with detailed discussions. Suggestions are provided on how to choose a model for solving the UTVCO problem better. 3) Tracking control of the robot manipulator is formulated as a UTVCO problem and studied with the GZNN model. According to the simulative and physical experiments, the task of tracking control is accomplished excellently by using the GZNN model.

To effectively diminish the kinetic energy dissipation, joint-angle drift, and joint-velocity discontinuity problems, and simultaneously achieve the end-effector position and direction control as well as the avoidance of joint-physical limits, a novel velocity layer weighted multicriteria optimization scheme is proposed, which outperforms the traditional schemes for the robotic manipulators with multiple constraints. Besides, considering that the existing joint-limit conversion strategies are not differentiable everywhere or with relatively complex formulation, a new exponential joint-limit conversion strategy is introduced to facilitate the dynamic quadratic programming reformulation of the proposed scheme. For easier numerical realization and real-time control, aided with a high-precision six-step extrapolated-backward discretization rule, a novel discretized zeroing neural network model is proposed to resolve the proposed scheme, which has higher precision than the existing neural network models. Finally, numerical and physical experiments are conducted to substantiate the efficacy, superiority, and practicability of the proposed scheme and model.

The effectiveness of continuous-time zeroing neural network (ZNN) (CZNN) in continuum robot control has been preliminarily verified. However, CZNN is not friendly to digital devices and hardware implementation. Although discrete ZNN (DZNN) has been investigated in other tasks, all the existing DZNNs are designed with fixed time steps, which cannot meet the needs of different tasks. This motivates us to develop a generic DZNN model with variable number of time steps and apply it to continuum robot control. In this article, we present a data-driven scheme based on CZNNs to learn the unknown kinematics of continuum robots and solve the kinematic control problem. Furthermore, a unified$m$-step forward discretization formula is derived to discretize the CZNN-based scheme into DZNN-based control algorithms. Finally, we take the 1-step and the 3-step algorithms as examples to show the discretization process, and verify their efficacy by simulations and experiments.

Visual servoing of robot manipulators can be viewed as an optimization problem and recurrent neural network is widely accepted as a powerful tool for solving optimization problems. Inspired by this, an adaptive neural network method is proposed for image-based visual servoing (IBVS) with uncertain parameters. It is the first work focused on IBVS simultaneously considering the uncertain camera configuration parameters and uncertain kinematics of robot manipulators in the framework of recurrent neural networks. Theoretical analysis including convergence and stability of the proposed method is presented. In order to verify the effectiveness of the theoretical results and the portability of the proposed method, simulations are conducted on different robot manipulators for different tracking tasks with excellent performance. In addition, comparisons with control schemes employing traditional gradient neural network (GNN), Kalman filter (KF) and model-based recurrent neural network highlight the great advantages of the proposed control system.

Differing from the common linear matrix equation, the future different-level linear matrix system is considered, which is much more interesting and challenging. Because of its complicated structure and future-computation characteristic, traditional methods for static and same-level systems may not be effective on this occasion. For solving this difficult future different-level linear matrix system, the continuous different-level linear matrix system is first considered. On the basis of the zeroing neural network (ZNN), the physical mathematical equivalency is thus proposed, which is called ZNN equivalency (ZE), and it is compared with the traditional concept of mathematical equivalence. Then, on the basis of ZE, the continuous-time synthesis (CTS) model is further developed. To satisfy the future-computation requirement of the future different-level linear matrix system, the 7-instant discrete-time synthesis (DTS) model is further attained by utilizing the high-precision 7-instant Zhang et al. discretization (ZeaD) formula. For a comparison, three different DTS models using three conventional ZeaD formulas are also presented. Meanwhile, the efficacy of the 7-instant DTS model is testified by the theoretical analyses. Finally, experimental results verify the brilliant performance of the 7-instant DTS model in solving the future different-level linear matrix system.

Tracking control of redundant and hyperredundant manipulators is a fundamental and critical problem in practical applications. In order to effectively decrease the end-effector position errors, a novel dual fuzzy-enhanced neurodynamic (DFEN) scheme is put forward for solving the position error accumulation problem followed by achieving accurate tracking control results. The proposed scheme is established based on a zeroing neurodynamic approach in conjunction with two fuzzy adjustment units that are capable of tuning the control parameters by monitoring the tracking error. Moreover, the DFEN scheme can effectively solve the tracking problem without requiring knowinga prioriknowledge of the kinematic model of the robot. The convergence and the stability of the proposed approach are demonstrated by theoretical analysis. The effectiveness, accuracy, and robustness of the proposed DFEN scheme are verified on the simulative redundant manipulator, continuum robot, and hybrid robot (integrating the redundant manipulator and the continuum robot). A practical experiment is provided to validate the proposed scheme as well.

In this article, the new jerk-level configuration adjustment (JLCA) schemes are proposed to achieve the configuration adjustment of constrained redundant robots. Specifically, by applying the zeroing neurodynamics design rule three times, the new JLCA performance index is first derived; then, together with the joint physical constraints incorporated, the new JLCA schemes are obtained for dual-arm and single-arm redundant robots, respectively. For comparison purposes, three other configuration adjustment schemes are also presented. Moreover, the comparative simulative experiments based on a planar dual-arm redundant robot (i.e., five-link dual-arm robot) and a spatial single-arm redundant robot (i.e., Kinova JACO$^2$robot) are performed to verify the efficacy and superiority of the proposed JLCA schemes, as compared with the three other configuration adjustment schemes. At last, the comparative physical experiments are conducted on the real Kinova JACO$^2$robot to substantiate the practicability and excellent performance of the proposed JLCA scheme for single-arm redundant robots.

Fault tolerance is a critical requirement for robust motion control of redundant robotic manipulators. This article aims to endow the redundant manipulator with the capability to achieve the required path of end-effector in the condition that one or some of its joints’ motion fail. Although many fault-tolerant control algorithms of redundant manipulator have been proposed in recent years. However, few of them are on the basis of the condition that the robotic model is unknown. The complexity of the calculation model limits the efficiency and portability of these algorithms. For the first time, we proposed a discrete model-free fault-tolerant tracking control scheme of redundant manipulator, which takes into account the fault tolerance in the control system of redundant manipulator by formulating it into a quadratic programming (QP) framework. The core of the proposed scheme consists of a discrete kinematic estimator and a discrete QP solver, powered by which the fault-tolerant control problem is transformed into a unified computing problem relaxing the need of knowing the redundant manipulator’s kinematic model. A discrete joint space observer is proposed for the detection of the happening of faulty states. Extensive simulations and experiments based on a redundant manipulator are performed and analyzed to support the verification of the efficiency and effectiveness of the proposed scheme.

Obstacle avoidance is usually an additional task for a redundant manipulator when the end-effector tracking task is performed, which guarantees the safety of the redundant manipulator. By formulating and combining the end-effector tracking task and the obstacle avoidance task using zeroing neural network (ZNN), in this article, a concise continuous ZNN (CZNN) controller is first proposed. To develop discrete controllers for practical control, a second-order discrete formula and a third-order discrete formula are introduced. Therefore, by utilizing two discrete formulas to discretize the CZNN controller, two concise discrete ZNN (DZNN) controllers are proposed. Detailed theoretical analyses guarantee the effectiveness of task formulations, CZNN controller, and DZNN controllers. In addition, some comparisons with existing studies are presented in details. Furthermore, three groups of simulative experiments on the basis of UR5 manipulator and two groups of physical experiments on the basis of Kinova manipulator are conducted to illustrate the effectiveness, superiority, and practicability of two DZNN controllers.

In this study, a continuous-time gradient-Zhang neural dynamics model is proposed for computing pseudoinverses (also termed as Moore-Penrose inverses) of time-varying matrices on the basis of gradient and Zhang neural dynamics. For hardware realization, the discrete-time gradient-Zhang neural dynamics (DTGZND) models, including DTGZND-1 and DTGZND-2 models, are proposed by using Zhang et al. discretization (ZeaD) and extrapolation formulas. Compared with five conventional Zhang neural dynamics models in numerical experiments, the proposed DTGZND-1 and DTGZND-2 models own high precision, avoid computing inverse explicitly, and also work out for challenging Z-type matrices. Besides, the DTGZND-1 model is applied to robot manipulator motion planning and control via simulative and physical experiments. The experimental results substantiate the correctness and effectiveness of the proposed models.

Bio-inspired robots, e.g., soft continuum robots, have broad application prospects due to their structural dexterity and interaction safety. But these features also bring great challenges to the precise control of soft continuum robots. In this work, we investigate how to achieve the kinematic control of soft continuum robots without knowing model parameters of the robots. To this end, a model-free scheme based on varying-parameter recurrent neural networks (VP-RNN) is proposed. The scheme involves two components, one of which solves the inverse kinematics problem based on a VP-RNN model, and the other employs another VP-RNN model to estimate the pseudo-inverse of Jacobian matrix of continuum robots. Finally, the feasibility and robustness of the proposed control strategy are validated by simulations, including comparisons with other methods and case study with jammed actuation.

Performing a successful robotic grasping to uncertain objects in unstructured environments is challenging. This study presents a new compliant soft robotic gripper for objects handling and cap manipulation through the coordination of three soft fingers and in-hand manipulation. The experiments are conducted to validate that the soft robotic gripper can successfully realize simultaneous grasping and capping manipulations with only one flexible shaft actuation for every single soft finger.Note to Practitioners—Uncertain object manipulation tasks pose significant challenges to a robotic gripper while grasping and capping unknown objects without damaging them. The existing rigid grippers have experienced flexible manipulation through multiple degrees of freedom (DoFs) by complex mechanical structures, and the soft gripper can realize stiffness-compliant manipulation differently. The proposed novel robotic in-hand manipulation can execute grasping and cap manipulation by a single flexible shaft to simultaneously achieve bending and rotational movements. The relationship between stretching force and finger’s curvature can enable a custom design for user-specific applications.

This paper introduces a systematical interval-based framework of inherent uncertainties and pose evaluation for a class of soft continuum robots driven by flexible shafts. A more general model of continuum robots driven by shaft tendons is extended from prior kinematic models. On top of the proposed model, the interval-based analysis is presented to analyze and characterize the pose of continuum robots considering uncertainties in kinematic parameters and joint inputs. A 3-D printed bending actuator driven by a flexible shaft is evaluated for case study based on the proposed interval-valued framework. This paper investigates and compares a couple of refinement methods and proposes a new way of sensitivity analysis of model parameters based on interval arithmetic. The kinematic and mechanics parameters are measured and identified experimentally with a representation of intervals. The in-plane motion experiment validates that the computed bounds can enclose all the measured tip positions with consideration of the measurement uncertainty. The method is also validated when external loading is exerted.

Ergonomics is the study of designing more human-friendly products, systems or processes for human. By extending this concept to robotics field, we propose robot ergonomics which is a transdisciplinary approach that brings together roboticists, product designers, and architects to solve numerous unsettled research problems or technology bottlenecks in robotics community through designing products for robots. This paper focuses on a case study of chair design for Roomba. Seven design criteria are proposed and intensive experiments are performed to validate the criteria using 22 chairs. Based on such empirical design strategy, three generic principles (i.e., observability, accessibility, and safety) of chair design are extracted for Roomba.

High positioning accuracy with micropositioning robots (MPRs) is required to successfully perform many complex tasks, such as microassembly, manipulation, and characterization of biological tissues and minimally invasive inspection and surgery. Despite the widespread use of high-resolution micro- and nanopositioning robots, there is very little knowledge about the real positioning accuracy that can be obtained and what the main influential factors are. Indeed, very few notable methods are available to measure multi-degree-of-freedom motions with adapted range, resolution, and dynamic capabilities. The main objective of this paper is to quantify the positioning accuracy of serial MPRs and to identify the main influential factors (a typical XY Θ serial robot is chosen as a case study). To reach this goal, a measuring system that combines vision and pseudoperiodic patterns with an extremely large range-to-resolution ratio is introduced as a new way to quantify the positioning accuracy of MPRs for in-plane motions. Then, an open-loop control approach based on MPR calibration is chosen for several reasons: the use of different models to identify influential factors, the quantification of the positioning accuracy, and the necessity of the method when sensor integration is too complex. Experiments using five different calibration models were conducted to classify factors influencing the positioning accuracy of MPRs. The results show that positioning accuracy can be improved by more than 35 times from 96 μ with no imperfection compensation to 2.5 μ by compensating for geometric, position-dependent, and angle-dependent errors through the MPR calibration approach.

Accuracy is an important issue for microrobotic applications. High accuracy is usually a necessary condition for reliable system performance. However there are many sources of inaccuracy acting on the microrobotic systems. Characterization and compensation enable reduction of the systematic errors of the micropositioning stages and improve the positioning accuracy. In this paper, we propose a novel method based on vision and pseudo-periodic encoded patterns to characterize the position-dependent errors along XY stages. This method is particularly suitable for microscale motion characterization thanks to its high range-to-resolution ratio and avoidance of camera calibration. Based on look-up tables and interpolation techniques, we perform compensation and get improved accuracy. The experimental results show an accuracy improved by 84% for square tracking and by 68% for random points reaching (respectively from 22 μm to 3.5 μm and from 22 μm to 7 μm).

In micromanipulation, especially microassembly, accuracy is a criterion useful for characterizing the performance of microrobots. The increase of positioning accuracy is a very important issue before making automatic assembly or other micro-tasks such as characterization. Thermal drift is one of the major sources of inaccuracy for automatic micromanipulation in ambient conditions and even in clean room. This paper addresses the calibration of a 1-DOF (Degree Of Freedom) nanopositioning cell including thermal drift compensation. The nanopositioning cell consists of a single-axis PZT stage and a XYZ manual stage which is usually used for fine positioning in microassembly platform. Subsequently, validations are implemented to test the performance by integrating the calibrated model. The experimental results show an effective improvement of the accuracy by a factor of 7 when temperature changes in the range of temperature for training, and 2.4 times improvement is achieved when temperature goes out of the training range.