Learning from Demonstration (LfD) allows novice users to teach robots through demonstrations without coding; however, such demonstrations are often suboptimal and can limit robot performance. To better support novices, we investigate the design of a feedback system that enables effective human-robot communication during demonstrations. We first conducted a focus group study (N = 9) to identify effective ways of visualizing key robot information, including joint limits, self-collisions, and manipulability. Guided by these insights, we designed an AR-based real-time feedback system and evaluated it in a between-subjects user study (N = 36) on a 7-DoF collaborative robot. Participants performed two tasks—insertion and pouring—with the second task enabling assessment of participants’ learning across tasks. Results show that real-time feedback reduced demonstration time, increased task completion rate, lowered perceived mental workload, and improved adherence to robot kinematic constraints. These findings demonstrate the effectiveness of the real-time feedback system for intuitive and effective robot teaching.

Learning from Demonstration allows robots to acquire skills from human demonstrations, making them more accessible to a wider range of users. Among different approaches, kinesthetic teaching allows humans to manipulate the robot joints directly, making it effective method for demonstrating constrained tasks. However, robots with kinematic redundancy enable multiple joint configurations to achieve a desired task, which could influence human teaching performance. One one hand, it could make it easier, allowing more freedom to demonstrate the task, but on the other, it also increases the number of joints that needs to be manipulated, potentially affecting cognitive and physical load of the demonstrator. Therefore, it is crucial to investigate how the number of degrees of redundancy (DoR) impact human performance during kinesthetic demonstrations, and then how these demonstrations influence robot performance. We simulated high and low DoR by locking one of the robot joint on a 7-DoF Panda robotic arm. We conducted a within-subject user study (N = 24) with two conditions: unconstrained condition (high DoR) and constrained condition (low DoR). We used a motion capture system to capture participants physical interaction with the robot when demonstrating two tasks: button pressing and cuboid block insertion. The results show that the robot’s DoR significantly affects mental workload, demonstration time, number of failed attempts, and physical interaction with the robot. Likewise, joint constraints significantly influence robot performance, measured by task completion using the learned model. These findings highlight the importance of considering robot DoR during demonstrating constrained tasks, allowing novice users to provide effective demonstrations.

Controlling foot placement is a key challenge in the use of assistive lower limb exoskeletons designed for those with motor impairments. Due to the mechanical flexibility of exoskeletons, users can intentionally manipulate the resulting step length without alteration of the exoskeleton’s reference trajectory. This is generally achieved by manually applying wrench upon the exoskeleton and the ground using crutches. This work sought to investigate this mechanism as a deliberate means to control foot placement. Ten nondisabled participants were asked to pilot a user-balanced exoskeleton to target step lengths of 0.1 to 0.4 m, with the exoskeleton trajectory unchanged throughout the experiment. Performance was evaluated by mean absolute error (MAE) and standard deviation (SD) of resulting step lengths. To explore the degree that these results might apply to users with impairments, participants were asked to minimize leg muscle activations during the experiment. Simultaneously, surface electromyography (sEMG) data were collected and normalized between resting (0.0) and unassisted walking (1.0). Activations ranged between 0.014 and 2.853, and were used to categorize participants into High muscle activation (HMA) and Low muscle activation (LMA) groups. The LMA group (median MAE 0.026 m, SD 0.028 m) performed differently compared to the HMA group (median MAE 0.021 m, SD 0.021 m), however, most participants achieved acceptable performance across all target step lengths, compared to a 0.05 m guideline. The results confirm that step length can be controlled through exoskeleton users’ manual efforts. Whilst the range of adjustments may vary with device and user, this could facilitate simplified exoskeleton control strategies and an intuitive method of user control.

Shared control systems aim to combine human and robot abilities to improve task performance. However, achieving optimal performance requires that the robot's level of assistance adjusts the operator's cognitive workload in response to the task difficulty. Understanding and dynamically adjusting this balance is crucial to maximizing efficiency and user satisfaction. In this paper, we propose a novel benchmarking method for shared control systems based on Fitts' Law to formally parameterize the difficulty level of a target-reaching task. With this we systematically quantify and model the effect of task difficulty (i.e. size and distance of target) and robot autonomy on task performance and operators' cognitive load and trust levels. Our empirical results (N=24) not only show that both task difficulty and robot autonomy influence task performance, but also that the performance can be modelled using these parameters, which may allow for the generalization of this relationship across more diverse setups. We also found that the users' perceived cognitive load and trust were influenced by these factors. Given the challenges in directly measuring cognitive load in real-time, our adapted Fitts' model presents a potential alternative approach to estimate cognitive load through determining the difficulty level of the task, with the assumption that greater task difficulty results in higher cognitive load levels. We hope that these insights and our proposed framework inspire future works to further investigate the generalizability of the method, ultimately enabling the benchmarking and systematic assessment of shared control quality and user impact, which will aid in the development of more effective and adaptable systems.

Process mining (PM)-based goal recognition (GR) techniques, which infer goals or targets based on sequences of observed actions, have shown efficacy in real-world engineering applications. This study explores the applicability of PM-based GR in identifying target poses for users employing powered transhumeral prosthetics. These prosthetics are designed to restore missing anatomical segments below the shoulder, including the hand. In this article, we aim to apply the GR techniques to identify the intended movements of users, enabling the motors on the powered transhumeral prosthesis to execute the desired motions precisely. In this way, a powered transhumeral prosthesis can assist individuals with disabilities in completing movement tasks. PM-based GR techniques were initially designed to infer goals from sequences of observed actions, where discrete event names represent actions. However, the electromyography electrodes and kinematic sensors on powered transhumeral prosthetic devices register sequences of continuous, real-valued data measurements. Therefore, we rely on methods to transform sensor data into discrete events and integrate these methods with the PM-based GR system to develop target pose recognition approaches. Two data transformation approaches are introduced. The first approach relies on the clustering of data measurements collected before the target pose is reached (the clustering approach). The second approach uses the time series of measurements collected while the dynamic user movement to perform linear discriminant analysis (LDA) classification and identify discrete events (the dynamic LDA approach). These methods are evaluated through offline and human-in-the-loop (online) experiments and compared with established techniques, such as static LDA, an LDA classification based on data collected at static target poses, and GR approaches based on neural networks. Real-time human-in-the-loop experiments further validate the effectiveness of the proposed methods, demonstrating that PM-based GR using the dynamic LDA classifier achieves superior F 1 score and balanced accuracy compared to state-of-the-art techniques.

Wearable sensors for hand gesture recognition have demonstrated significant potential for creating non-invasive human-machine interfaces. Nonetheless, the trade-off between wearability, practicality and performance constrains their applicability in real-world scenarios. This paper introduces MyoLog, a wearable soft sensor system that utilises forearm muscle deformations for accurate hand gesture recognition. Muscle displacements are captured using an array of magnets and tri-axis magnetometers (displacement myography), integrated into soft and flexible structures that conform to and deform with the shape of forearm muscles. The high signal-to-noise ratio and sensitivity of the sensor modules in MyoLog produce information-rich signals, enabling the detection and differentiation of a wide spectrum of hand gestures. The study used the results of 9 participants performing 44 diverse gestures with MyoLog to investigate its performance in terms of number of gestures and achieved classification accuracy. The average performance achieved by participants was 97.7%, 91.5%, and 89.1% accuracy in executing 13, 22, and 28 gestures, respectively. To demonstrate the capabilities of MyoLog in practical settings, we explored two potential applications in virtual reality training for laparoscopic surgery and prosthetic hand control. The high wearability of MyoLog without compromising the performance paves the way for more practical human-machine interactions in diverse applications.

Haptic interaction is critical in physical human–robot Interaction (pHRI), given its wide applications in manufacturing, medical and healthcare, and various industry tasks. A stable haptic interface is always needed while the human operator interacts with the robot. Passivity-based approaches have been widely utilized in the control design as a sufficient condition for stability. However, it is a conservative approach which therefore sacrifices performance to maintain stability. This article proposes a novel concept to characterize an ultimately passive system, which can achieve the boundedness of the energy in the steady-state. A so-called ultimately passive controller (UPC) is then proposed. This algorithm switches the system between a nominal mode for keeping desired performance and a conservative mode when needed to remain stable. An experimental evaluation on two robotic systems, one admittance-based and one impedance-based, demonstrates the potential interest of the proposed framework compared to existing approaches. The results demonstrate the possibility of UPC in finding a more aggressive tradeoff between haptic performance and system stability, while still providing a stability guarantee.

Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks. One crucial aspect of neural growth is determining the optimal growth timing. However, few studies investigate this systematically. Our study reveals that neural growth inherently exhibits a regularization effect, whose intensity is influenced by the chosen policy for growth timing. While this regularization effect may mitigate the overfitting risk of the model, it may lead to a notable accuracy drop when the model underfits. Yet, current approaches have not addressed this issue due to their lack of consideration of the regularization effect from neural growth. Motivated by these findings, we propose an under/over fitting risk-aware growth timing policy, which automatically adjusts the growth timing informed by the level of potential under/overfitting risks to address both risks. Comprehensive experiments conducted using CIFAR-10/100 and ImageNet datasets show that the proposed policy achieves accuracy improvements of up to 1.3% in models prone to underfitting while achieving similar accuracies in models suffering from overfitting compared to the existing methods.

Learning from Demonstration (LfD) empowers novice users to teach robots daily life tasks without writing sophisticated code, thereby promoting the democratization of robotics. However, novice users often provide sub-optimal demonstrations, which can potentially impact the robot’s ability to efficiently learn and execute the tasks. Prior research has assessed the quality of demonstrations by evaluating the robot’s task performance; however, the approach remains insufficient to qualify individual demonstrations, leaving the reason for classifying demonstrations as high- or low-quality unknown. Therefore, this simulation-based study aims to quantify the quality of individual demonstration at each step by incorporating motion-related quality features such as manipulability and joint-space jerk. To assess the efficacy of these features, we initially evaluated the given demonstrations—taking into account each quality feature—to rank them from high- to low-quality. Subsequently, we investigated the impact of demonstration’s quality on task performance and the quality of task execution. In this pursuit, we trained a series of LfD models for distinct manipulation tasks: cube lifting and pick-and-place of soda can. Our results illustrate a strong correlation between ranked demonstrations and the quality of task execution. Interestingly, we observed that the quality features have a significant impact on task performance, particularly when the provided demonstrations exhibit diversity in terms of quality. Overall, this analysis enables quantifying the quality of individual demonstrations based on motion-related quality features, thus improving learning from demonstration.

A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses.

Performance in an engineering system tends to degrade over time due to a variety of wearing or ageing processes. In supervisory controlled processes there are typically many signals being monitored that may help to characterize performance degradation. It is preferred to select the least amount of information to obtain high quality of predictive analysis from a large amount of collected data, in which labeling the data is not always feasible. To this end a novel unsupervised feature selection method, robust with respect to significant measurement disturbances, is proposed using the notion of "weak monotonicity" (WM). The robustness of this notion makes it very attractive to identify the common trend in the presence of measurement noises and population variation from the collected data. Based on WM, a novel suitability indicator is proposed to evaluate the performance of each feature. This new indicator is then used to select the key features that contribute to the WM of a family of processes when noises and variations among processes exist. In order to evaluate the performance of the proposed framework of the WM and suitability, a comparative study with other nine state-of-the-arts unsupervised feature evaluation and selection methods is carried out on well-known benchmark datasets. The results show a promising performance of the proposed framework on unsupervised feature evaluation in the presence of measurement noises and population variations.

An interface for a powered upper limb prosthesis is designed to achieve the target poses intended by the human user as interpreted from input features measured from a variety of sensors typically worn on the residual limb. Selection of which input features to use in the interface is usually done based on which features provide the best likelihood to identify the intended target poses. This work investigates the outcomes of the prosthetic interfaces constructed with input features selected based on the performance of all subjects (population-averaged) vs. the performance of individual subjects (personalised). Specifically, two outcomes are evaluated. First, the diversity in the resulting input feature sets across different subjects is evaluated. Second, the accuracy in identifying the intended target pose using the input features selected through personalised and population-averaged data are analysed and compared. The experiment was conducted with 10 able-bodied subjects in a virtual reality (VR) platform. The corresponding kinematic information of their arm movements and muscle activities were recorded and utilised to construct the input features in the scenario of transhumeral prostheses. The outcomes show a significant variation in the resulting input feature sets from subject to subject. The accuracy of the personalised feature sets is found to be higher in classifying the intended target poses. For the case of selecting 2 out of the 41 input features constructed from the recorded sensor data, none of the 10 subjects shares any common set of input features and the accuracy is improved by 10.4±6.8% with the personalised selection. The outcomes demonstrate the extent of person-to-person variation that needs to be taken into account when designing prosthetic interfaces and the quantified potential gain (albeit in a specific application) from personalising interfaces.

Synergies have been adopted in prosthetic limb applications to reduce the complexity of design, but typically involve a single synergy setting for a population and ignore individual preference or adaptation capacity. However, personalization of the synergy setting is necessary for the effective operation of the prosthetic device. Two major challenges hinder the personalization of synergies in human-prosthesis interfaces (HPIs). The first is related to the process of human motor adaptation and the second to the variation in motor learning dynamics of individuals. In this paper, a systematic personalization of kinematic synergies for HPIs using online measurements from each individual is proposed. The task of reaching using the upper limb is described by an objective function and the interface is parameterized by a kinematic synergy. Consequently, personalizing the interface for a given individual can be formulated as finding an optimal personalized parameter. A structure to model the observed motor behavior that allows for the personalized traits of motor preference and motor learning is proposed, and subsequently used in an online optimization scheme to identify the synergies for an individual. The knowledge of the common features contained in the model enables online adaptation of the HPI to happen concurrently to human motor adaptation without the need to retune the personalization algorithm for each individual. Human-in-the-loop experimental results with able-bodied subjects, performed in a virtual reality environment to emulate amputation and prosthesis use, show that the proposed personalization algorithm was effective in obtaining optimal synergies with a fast uniform convergence speed across a group of individuals.

This paper aims to characterise the magnetic interaction in neighbouring sets of local electromagnetic actuation (LEMA) actuators in a robotic platform for abdominal surgery. The analysis looks into the affect of the magnetic fields contributed by a stator-rotor set (the actuation unit) located adjacent to the rotor of interest. Each rotor drives one of the degree-of-freedoms (DOFs) on a surgical robotic device. In this study, a two-DOF setup is used for the magnetic interaction analysis, which can be expanded to general case n-DOF setup with the Principle of Superposition of magnetic fields from multiple sources. The magnetic model is then used to compute the dynamics of the system, which involves the equation of motion of the rotors and associated robotic mechanism it drives, and the actuator (electrical) model that takes into account the back EMF generated by the permanent magnet rotors. The magnetic field effect of the neighbouring set onto the rotor is observed by obtaining the speed response of the rotor through simulation so that the dynamic model can be validated against the experimental results. The outcomes are useful for the design specification of the LEMA system configuration, involving the feasible / pragmatic distance between the stator sets such that the interference is minimised, and for the design of the necessary control strategy.

This paper aims to establish a post processing algorithm to estimate the upper limb motion, given a set of measurements from wearable sensors representing the orientation of the shoulder, upper arm and lower arm. The motivation of the development is the measurement of the upper limb motion for subjects with motor impairments, such as post-stroke patients preventing the use of specific motions for calibration purposes and allowing the sensors to be relatively insensitive to their mounting positions. The type of sensors has been left general, with the experimental validation in this paper carried out using inertial sensors and magnetic trackers. The method is validated both numerically and experimentally, and shows improvements compared to the common inverse kinematics approach, especially in the practical conditions where sensor mounting alignment is suboptimal.

In this paper, a model predictive control is developed for motion planning and control of nonholonomic autonomous forklifts. The proposed model predictive motion control (MPMC) determines the control inputs for tracking a desired path through minimising path-following error, subject to nonholonomic and dynamic balance constraints of the forklift. MPMC also automatically adjusts the tracking velocity and acceleration to traverse the path as fast as possible without violating constraints while minimising the tracking error. In order to facilitate the real-time implementation of MPMC for industrial applications, a successive online linearisation of the nonlinear discrete-time kinematic model of the forklift is employed along with linear time-varying approximation of the tracking errors. The effectiveness of the proposed method has been demonstrated through simulations.

In this work, a simple model is used to characterize the learning behaviour of humans. Based on this model, it is possible to define a similarity measure between two tasks in order to quantify skill generalisation during the learning of simple motor tasks by humans. By fully exploring this similarity measure, a sequence of tasks capable of improving the learning efficiency for both healthy subjects and patients with motor impairment may be generated. A validation protocol is introduced and preliminary experimental results with six subjects are presented to validate the learning model and the similarity measure. Results show that the human learning of trajectory tracking tasks can accurately be modelled by an exponential decay of the average tracking error. The model fits well when the task is new or far away from a previously learnt task. Model parameters are used to analyse the learning performances of the subjects and the influence of previous tasks learning. Finally, it is shown that the similarity index can be constructed based on the proposed model to reflect skill generalisation.

The study of cable-driven parallel robots (CDPRs) has attracted much attention in recent years. However, to the best of the authors' knowledge, no single software platform exists for researchers to perform different types of analyses for CDPRs of arbitrary structure. In this paper, the Cable-robot Analysis and Simulation Platform for Research (CASPR) of CDPRs is introduced. Using this platform, arbitrary types and structures of CDPRs, such as single and multi-link CDPRs, can be studied for a wide range of analyses, including kinematics, dynamics, control and workspace analysis. CASPR achieves this using a general CDPR model representation and an abstracted software architecture. Moveover, CDPRs can be defined using Extensible Markup Language (XML) with out-of-the-box availability of an extensive range of robots and analysis tools. The open-source platform aims to provide both a communal environment for the researchers to use and add models and algorithms to. The example case studies demonstrate the potential to perform analysis on CDPRs, directly compare algorithms and conveniently add new models and analyses.

The manipulation of objects is an inherent capability of dexterous human hands. However, state-of-the-art prostheses are limited to only forming grasps and gestures due to the low information transfer rate in the human-machine interface. Additionally, in the prosthetic setting the hand is not privy to such global knowledge including the object's shape, weight, friction properties, etc. Existing techniques from robotics that can manipulate objects in this prosthetic setting require compensation terms which are counterproductive to the manipulation task. The incorporation of tactile sensing leads to a simpler control formulation and enables relaxation of several restrictive assumptions inherent in the robotics applications. In this work a novel control system is proposed that incorporates tactile sensing for object manipulation and optimal distribution of contact forces for prosthetic hands. Simulation results demonstrate the ability of the proposed control system to manipulate an object, while minimizing the use of frictional forces during manipulation.

Fluid motion planners are a type of artificial potential field (APF) motion planners that use the differential equations of fluid flow to determine the desired trajectory. The fluid flow approach in motion planning can efficiently produce natural-looking trajectories. However, the differential equations used in previous studies are restricted to motion planning in 2-D environments. In this paper, the fluid flow approach is extended to a motion planning framework for 3-D mobile robots that avoids spheroidal obstacles. Compared with existing APF approaches, kinematic constraints in both speed and curvature are also considered. Possessing the efficiency of 2-D fluid motion planners, the proposed approach is able to plan natural-looking reference trajectories for nonholonomic 3-D mobile robots. The approach is demonstrated through various 3-D example scenarios. The work can be considered as a fundamental framework for 3-D fluid motion planning, where additional kinematic constraints and more complex scenarios can be incorporated.

Joint interaction forces and moments play a significant role within multilink cable-driven manipulators (MCDMs). In this paper, the consideration of joint interaction forces and moments in the objective function and constraints specific to the inverse dynamics of MCDMs are considered for the first time. By formulating the relationship between the joint interactions and cable forces, it is shown that the minimization of the joint interactions results in a convex quadratic program. Furthermore, the inclusion of constraints to maintain the stability of unilateral spherical joints results in a quadratically constrained quadratic program. Simulation results of the proposed formulations on two-link eight-cable and eight-link 76-cable manipulators are compared with the traditional two-norm cable force minimization. Results show that the formulations are able to take advantage of the actuation redundancy in considering the joint interactions within the inverse dynamics of MCDMs.

Multilink cable-driven manipulators offer the compactness of serial mechanisms while benefitting from the advantages of cable-actuated systems. One major challenge in modeling multilink cable-driven manipulators is that the number of combinations in the possible cable-routing increases exponentially with the number of rigid bodies. In this paper, a generalized model for multilink cable-driven serial manipulators with an arbitrary number of links that allow for arbitrary cable routing is presented. Introducing the cable-routing matrix (CRM), it is shown that all possible cable routing can be encapsulated into a single representation. The kinematics and dynamics for the generalized model are derived with respect to the CRM. The advantages of the proposed representation include the simplicity and convenience in modeling and analysis, where all cable routing is inherently considered in a single model. To illustrate this, the inverse dynamics analysis is performed for two example systems: a 2-link 4-DoF manipulator that is actuated by 6 cables and an 8-link 24-DoF mechanism actuated by 76 cables. The results show the validity and scalability of the generalized formulation, allowing for complex systems with arbitrary cable routing to be modeled and analyzed.

Human motor control computational model is an important component in the study and the successful realisation of human-robot interaction. In this paper, the Operational Space Formulation is presented as a suitable framework of human motor control computational model based on the Equilibrium Point Hypothesis (EPH) approach. The iterative adaptive control strategy was incorporated to simulate human motor adaptation to different tasks. The strategy involves the use of an Equilibrium Model which represents the ideal human motor response to a given task. The combined strategy was simulated to match a set of data gathered experimentally from several human subjects. The results were observed to explain many of the features found in the recorded behaviours in the EPH-based approach of human motor modelling.

An automated design generation algorithm for a serial kinematic chain is presented for the reconfigurable robot used in a novel endoluminal surgical procedure (European Union project ARES). The algorithm produces the possible topologies, given the design constraints, desired performance, and available modules, such that all constraints are satisfied for every point in the desired workspace. This is achieved through the use of interval analysis methods and branch-and-bound loop that searches through the end-effector pose and the design parameter spaces. The resulting algorithm is demonstrated through an example of a serial chain manipulator made of the reconfigurable modules of the surgical robot for the application. The results are presented and discussed.

This paper presents the design strategy of serial manipulators with constraint satisfaction. The algorithm provides certified solutions to the range of values of the manipulator design parameters that satisfy the given constraints for all points inside a desired workspace. Alternatively, it can also be used to obtain the achievable workspace of a particular manipulator topology within which a set of given constraints are satisfied. This strategy can therefore be applied to the general case of a serial manipulator design problem, robots of adjustable parameters, or even reconfigurable robot strategy to obtain a suitable topology. The interval-based algorithm was implemented on an example serial anthropomorphic manipulator with joint displacement constraints and obtains the possible variations to the manipulator topology that allow the required workspace to be achievable under the given joint displacement constraints. Results are presented and discussed.

This paper addresses the problem of certifying the performance of a precision flexure-base mechanism design with respect to the given constraints. Due to the stringent requirements associated with the applications of flexure-based precision mechanisms, it is necessary to be able to evaluate and certify the performance at the design stage, taking into account the possible sources of errors: such as fabrication tolerance and modeling inaccuracies in flexure joints. An interval-based method is proposed to certify whether various constraints are satisfied for all points within a required workspace. This paper presents the interval-based methodology and its implementation on a planar 3RRR parallel flexure-based manipulator.

This paper proposes and evaluates an adaptive technique and a variable structure control approach for piezoelectric actuation systems to track specified motion trajectories. The proposed control methodologies are formulated to accommodate unknown or uncertain system parameters, nonlinearities including the hysteresis effect, and external disturbances in the piezoelectric actuation systems without any form of feed-forward compensation. In this study, both control methodologies are demonstrated to possess a promising motion tracking ability experimentally. In comparison, the variable structure control approach is evaluated to be superior to the adaptive technique in the motion tracking control. With the ability to track motion trajectories under parametric uncertainties, nonlinearities, and external disturbances, the proposed control methodologies are very attractive in realising the high-precision piezoelectric actuation systems for micro/nano manipulation

This paper presents the direct kinematic solutions to 3DOF planar parallel mechanisms. Efforts to solve the direct kinematics of planar parallel mechanisms have concentrated on RPR mechanisms due to its inherent simplicity. It is established that the direct kinematic equations of a general 3DOF planar parallel mechanism can be reduced to a univariate polynomial of degree 8. This paper presents the derivation of this univariate polynomials for both 3RRR and 3RPR mechanisms, showing the similarities and differences between the two common configurations of 3DOF planar parallel mechanisms. This paper also presents the on the direct kinematic solution to a simplified case of the 3RRR planar parallel mechanisms, where it is possible to decouple the polynomial further into two quadratic equations, describing the position and orientation of the end-effector, respectively. This result will provide an efficient computation method for a very useful configuration of planar parallel manipulators

This paper proposes a robust control methodology for piezoelectric actuation systems to track specified motion trajectories. This is motivated by the search for an effective control strategy to deal with the problem of nonlinear behaviour in the piezoelectric actuation systems. The basic concept associated with this approach lies in the specification of a target performance and the formulation of a robust control scheme for the system to ensure the convergence of the position tracking error to zero in the presence of parametric uncertainties and hysteresis effect inclusive of other un-modelled disturbances. Stability of the control system is proven theoretically and the robust control methodology is demonstrated to possess a promising tracking ability through the control experiments. Implementation of the control law requires only a knowledge of the estimated parameters and their corresponding bounds as well as the bound of the hysteresis effect including disturbances. Being capable of handling uncertainties and disturbances, the robust control methodology is very attractive in the field of micro/nanomanipulation in which high-precision control applications could be realised

Kinematic isotropy of omnidirectional mobile robots with powered caster wheels is discussed and utilized in optimizing the design parameters of the robots. The analysis is done to cover the most general cases, by observing the mathematical expression of the equations of motion rather than by numerical evaluation of specific parameters or actuation schemes. Optimal design guidelines are provided. Singularity analysis is given to support the proposed design guidelines. Simulation results are presented to verify the conclusions drawn.

The Operational Space Formulation provides a framework for the analysis and control of manipulator systems with respect to the behavior of their end-effectors. Its application to aircraft canopy polishing is shown using a mobile manipulator. The mobile manipulator end-effector maintains a desired force normal to the canopy surface of unknown geometry in doing a compliant polishing motion, while, at the same time, its mobile base moves around the shop floor, effectively increasing the mobile manipulator's workspace. The mobile manipulator consists of a PUMA 560 mounted on top of a Nomad XR4000. Implementation issues are discussed and simultaneous motion and force regulation results are shown.

A singularity handling method is proposed in this paper. It is done by introducing virtual redundant joints into the Jacobian matrix to maintain the rank of the Jacobian matrix when singularity occurs. These additional joints do not exist physically. Therefore, although mathematically stable, the manipulator still cannot perform tasks in the degenerate direction(s). This method is comparatively straight forward to implement and it does not have a singular subspace defined within which a special and different control algorithm is performed, thus it avoids the problem associated with discontinuous control or switching of control. The method was tested on simulation and implemented in real-time on the PUMA 560 robot.