Retinal surgery is one of the most challenging types of surgery because of the scale and the fragility of the human eye anatomy. The surgeon suffers from limited positioning accuracy, tremor and poor force feedback directly affecting the quality of the surgical procedures. To tackle these issues, we developed a teleoperation system to assist surgeons during retinal surgery. The system offers features like motion scaling, tremor compensation and scaled force feedback. This paper reports on the design of the slave and the master.

Navigating an electrical wheelchair can be very challenging due to its large size and limited maneuverability. Additionally, target users often suffer from cognitive or physical disabilities, which interfere with safe navigation. Therefore, a robotic wheelchair that helps to drive can prove invaluable. Such a wheelchair shares the control with its human operator. Typically, robots excel in fine-motion control whereas users want to remain in charge. Hence, the robot should focus its help locally and let the user decide about global behavior. Further, an effective robot should understand the navigation plans of its user. It needs to consider the user's abilities to avoid frustrating the user with wrong assistance. In order to address these requirements, we propose a probabilistic framework to recognize local navigation plans in a user-specific way. The framework infers navigation plans online and provides a method to calibrate all model parameters from real driving data. It fuses past local information with a user-specific model to reason about how and where the user intends to navigate. We illustrate the validity of our approach by recognizing the local navigation plans of a spastic user driving in a daily environment.

This paper describes a force-measuring manipulator for minimally invasive surgery. It employs an extracorporeal force measurement system combined with a passive trocar support to deal with the restrictions of a single insertion point in the body. The first contribution is the detailed description of the design and the involved kinematics of the device and the force measurement system, while the second contribution is an extensive performance evaluation including an in vivo animal experiment. During the latter, endoscopic video images were recorded simultaneously with motion and force information of the instrument tip. Breathing effects, contact with other instruments and nonlinear soft tissue stiffness could be detected by the system, indicating its potential as a MIS manipulator and as an in vivo tissue property measurement tool.

Retinal Vein Occlusion (RVO) is a common retinal vascular disorder which may cause severe loss of vision. Retinal cannulation appears to be the most effective treatment, but given the small diameter of a retinal vein, it is too difficult and risky for a surgeon to perform this procedure manually. This work reports on the development of an innovative robotic manipulator to assist vitreoretinal surgeons during this procedure using a co-manipulation control strategy. The robotic manipulator features a new Remote-Center-of-Motion mechanism with four degrees of freedom. This mechanism is particularly interesting for applications in minimally invasive surgery where an instrument needs to be manoeuvred in a highly confined space around a fixed incision point. The developed manipulator is shown to be a great asset in improving the quality of retinal cannulations compared to the manual procedure. This is shown by cannulation experiments performed on a custom made eye model and an injectable retina model that effectively simulate real retinal cannulations.

This paper analyses and compares two passivity-based approaches that allow to include a-priori knowledge on the dynamic range of the human operator and/or the environment. This can lead to less conservative teleoperation systems compared to systems designed to be purely passive or absolutely stable. The first approach under investigation is a method where the absolute stability is analysed of a teleoperation system augmented with shunt impedances in series and/or parallel with the teleoperation system. It is shown that the traditional interpretation of the use of shunt impedances is not valid and a more accurate description of how to use this method is presented. The second approach under investigation is the bounded environment (operator) method. It is shown that the original idea to restrict the analysis to the so-called worst-case scenarios of a pure mass and a pure stiffness as environment can be too simplistic. Illustrative examples with mass-spring-damper systems fixed to the ground and floating objects as environments are made to demonstrate this in detail. In conclusion, this paper shows that embedding environment knowledge into the controller analysis/design is not straightforward and further research should be dedicated to determine which bounds should be used to obtain practically stable systems for different applications.

In this paper, the Bounded Environment Passivity method is applied to a 3-channel controller. This method enables the design of teleoperation controllers that show passive behaviour for interactions with a bounded range of environments.The resulting tuning guidelines, derived analytically, provide interesting tuning flexibility, which allows to focus on different aspects of transparency. As telesurgery is the motivation behind this work, the focus lies on correctly reflecting the stiffness properties of the environment. A comparison between the transparency and stability properties of this 3-channel controller and the same properties of the Position-Force controller demonstrates the interesting properties o fthe 3-channel controller. The theoretical results are verified experimentally on a 1 d.o.f. master-slave setup.

To process the large amounts of data industrial systems are producing nowadays, machine learning techniques have shown their usefulness in many applications. As the amounts of data being generated are getting huge, the need for machine learning metho

For people with very severe motor dysfunctions, Brain-Computer Interfaces (BCIs) may provide the solution to regain mobility and manipulation capabilities. Unfortunately, BCIs are characterized by a limited bandwidth and uncertainty on the BCI output. In the past, we have developed a Bayesian plan recognition framework that estimates from uncertain human-robot interface signals the task a robot should execute. This paper extends our plan recognition framework to incorporate uncertain BCI signals. A benchmark test is proposed and adopted to evaluate both the plan recognition framework and the performance of the BCI user, for the concrete application of wheelchair driving.

This paper derives analytic guidelines to tune the popular Position-Force bilateral controller and improve its performance by incorporating available knowledge on the bounds of the environment impedance. The proposed guidelines can prove especially useful in the domain of telesurgery where a need exists for well-understood bilateral teleoperation controllers, that show good performance and where many tasks can be characterized by restricted and relatively easily definable impedance regions. This paper firstly analyses the two-port passivity and absolute stability properties of two alternatives of the Position-Force controller. The limitations on achievable performance when guaranteeing absolute stability with arbitrary environments are detailed. Next, a novel method, called Bounded Environment Passivity method is introduced. This method enables the design of teleoperation controllers that show passive behaviour for interactions with an environment that varies over a given range of impedances. A set of guidelines that allow a smarter trade-off between performance and stability follows. The theoretical results are verified experimentally on a 1-d.o.f. teleoperation setup.

This paper presents a novel design of visual state estimation for an image-based tracking control system to estimate system state during visual tracking control process. The advantage of this design is that it can estimate the target status and target image velocity without using the knowledge of target’s 3D motion-model information. This advantage is helpful for real-time visual tracking controller design. In order to increase the robustness against random observation noise, a neural network based self-tuning algorithm is proposed using echo state network (ESN) technique. The visual state estimator is designed by combining a Kalman filter with the ESN-based self-tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several interesting experiments on a mobile robot validate the proposed algorithms.

Many elderly and disabled people experience difficulties when maneuvering an electric wheelchair. In order to make wheelchair driving a safer and more comfortable experience, there has long been the claim to equip wheelchairs with some form of intelligent controller assisting in difficult or unsafe situations. It has been observed that every user presents different symptoms causing a specific driving pattern. Therefore, if the user is to be helped and not frustrated, his/her particular driving behavior should be taken into account when assisting him/her. In this paper we present a general user modeling technique for our Bayesian framework for plan recognition and shared wheelchair control. Plan recognition corresponds to estimating the plan a user has in mind. Assistive actions can then be taken based on the estimated user plan. A user modeling technique based on Gaussian processes has been selected, which can be adapted online to any type of driving style. The potential of Gaussian processes for user modeling is illustrated on a case study with a disabled patient suffering from spastic quadriplegia.

A first step towards truly versatile robot assistants consists of building up experience with simple tasks such as the cooperative manipulation of objects. This paper extends the state-of-the-art by developing an assistant which actively cooperates during the point-to-point transportation of an object. Besides using admittance control to react to interaction forces generated by its operator, the robot estimates the intended human motion and uses this identified motion to move along with the operator. The offered level of assistance can be scaled, which is vital to give the operator the opportunity to gradually learn how to interact with the system. Experiments revealed that, while the robot is programmed to adapt to the human motion, the operator also adapts to the offered assistance. When using the robot assistant the required forces to move the load are greatly reduced and the operators report that the assistance feels comfortable and natural.

The last years have witnessed a significant increase in the percentage of old and disabled people. Members of this population group very often require extensive help for performing daily tasks like moving around or grasping objects. Unfortunately, assistive technology is not always available to people needing it. For instance, steering a wheelchair can represent an extremely fatiguing or simply impossible task to many elderly or disabled users. Most of the existing assistance platforms try to help users without considering their specific needs. However, driving performance may vary considerably across users due to different pathologies or just due to temporary effects like fatigue. Therefore, we propose in this paper a user adapted shared control approach aimed at helping users in driving a power wheelchair. Adaption to the user is achieved by estimating the user's true intent out of potentially noisy steering signals before assisting him/her. The user's driving performance is explicitly modeled in order to recognize the user's intention or plan together with the uncertainty on it. Safe navigation is achieved by merging the potentially noisy input of the user with fine motion trajectories computed online by a 3D planner. Encouraging results on assisting a user who cannot steer to the left are reported on K.U.Leuven's intelligent wheelchair Sharioto.

Manufacturing control systems that are built along the Holonic Manufacturing Execution Systems (HMES) approach, are able to generate short-term forecasts (resource loads and order routings). However, the reliability of these forecasts has to be balanced against the reactivity of the system in coping with the changes and disturbances that happen in the manufacturing environment. These phenomena are captured in so-called system nervousness issues. This paper discusses the system nervousness issues and how to handle them.

Many elderly and disabled people today experience difficulties when manoeuvring an electric wheelchair. In order to help these people, several robotic assistance platforms have been devised in the past. In most cases, these platforms consist of separate assistance modes, and heuristic rules are used to automatically decide which assistance mode should be selected in each time step. As these decision rules are often hard-coded and do not take uncertainty regarding the user's intent into account, assistive actions may lead to confusion or even irritation if the user's actual plans do not correspond to the assistive system's behavior. In contrast to previous approaches, this paper presents a more user-centered approach for recognizing the intent of wheelchair drivers, which explicitly estimates the uncertainty on the user's intent. The paper shows the benefit of estimating this uncertainty using experimental results with our wheelchair platform Sharioto

This paper describes a novel adaptive filter approach to reduce the handicap a patient may experience when navigating an electric wheelchair. The filter automatically adapts to the specific handicap the patient has by training a connectionist structure that converts the joystick signal of the patient to the signal a reference user would give in the same context. Experimental results show that for various handicaps the filter improves the driving performance significantly

This paper presents an extension of current global dynamic window approaches to holonomic and nonholonomic mobile robots with an arbitrary cross-section. The algorithm proceeds in two stages. In order to account for an arbitrary robot footprint, the first stage takes the robot's orientation explicitly into account by constructing a navigation function in the (x, y, /spl theta/) configuration space. In a second stage, an admissible velocity is chosen from a window around the robot's current velocity, which contains all velocities that can be reached under the acceleration constraints. Fast computation over large areas is achieved by adopting multi-resolution (x, y) and (x, y, /spl theta/) planning. Several measures are taken to obtain safe and robust robot behaviour. Experimental results on our wheelchair test platform show the feasibility of the approach.

This paper presents our approach to extend the niche of behavior-based robotics toward manipulation. We use results from neuroscience to derive some qualitative design rules for the mechanics of the manipulator, resulting in a next-generation manipulator, the "soft arm". By defining the basic behaviors of the manipulator as trajectory-producing behaviors (which is also biologically plausible), we have designed a first test case: writing on a board with a mobile manipulator. The soft arm has not yet been developed; therefore, we have emulated such a soft robot arm on an industrial robot.

This paper addresses the influence of friction on the haptic transparency within soft tissue telemanipulation. In contrast to remote handling of hard environments, the level of the friction forces is thereby within the same order of magnitude as the interaction forces. Therefore, these friction forces may alter performance and haptic transparency considerably. Control schemes showing nice results within simulations using linear models, with frictionless virtual manipulators in a virtual environment, or where friction forces are negligible compared to the interaction forces, show totally different results when implemented on a real telemanipulator setup with a realistic friction level and in contact with a soft environment. In this paper, friction is added to the linear models of the master and slave actuators as an input force disturbance. The influence of these disturbances on haptic transparency then is investigated analytically for the most widely used teleoperator schemes. Thereby, effectiveness of using force sensors on master and/or slave side is evaluated, and validated through experiments on a one-dimensional test setup.

This paper describes an advanced tool guiding system for robot assisted surgery. It is offering two additional local degrees of freedom to a standard robotic tool guiding system like of the Zeus robotic surgery system. The tool guide is basically a tube that guides the inserted surgical instrument to the desired location. By adding two bending degrees of freedom at the tip, the developed system largely increases the maneuverability of the instrument. It consists of a micromachined superelastic tube driven by a conventional cable system. Main issue was the design of a flexible hinge system in superelastic NiTi that offers the desired bending angles (at least 90 degrees in both directions) and that is compatible with the material limits for long-time cycling. Based on extended FE calculations and experimental measurements the second-generation design offers 90 degrees bending in both directions. The improvement over previous devices is the combination of two degrees of freedom with a small diameter of only 5 mm. An additional advantage is the tool channel, which enables the use of different instruments with this single device.

The aim of this research is to apply a laparoscopic robot to improve and facilitate the procedures of laser laparoscopy performed by gynecological surgeons. We took advantages of the existing laparoscopic robot and made digitizer tablet and wireless pen as intuitive interface for this particular purpose. The What-You-Draw-is-What-You-Cut (WYDIWYC) control scheme provide surgeons a natural interface to perform laser ablation as he is sketching by pen. The auto-focus functionality helps surgeons to focus the laser automatically by an laparoscopic distance sensor. Some safety measures are installed in DSP controller. The accuracy of the robot is examined by a sinusoidal test and is more than the demand in laser laparoscopy. The simplicity of usage of the intuitive writing interface is demonstrated by a volunteer and shows the excellent results by cutting on an apple. Some considerations for safety issues are discussed in this paper.

This paper presents our approach to extending the niche of behavior-based robotics to manipulation. We use results from neuroscience to define the basic behaviors of the manipulator. Furthermore, we derive some qualitative design rules for the mechanics of the manipulator. With these principles, we have designed a first demo application: writing on a board with a mobile manipulator.

Many elderly and disabled people today experience difficulties when maneuvering an electric wheelchair. In order to help these people, several robotic assistance platforms have been devised in the past. These platforms' architectures usually consist of separate assistance modes that each realise a specific navigation behaviour, such as "avoid-obstacles", or "drive-through-door". In most cases, heuristic rules are used to decide automatically which assistance mode should be selected in each time step. These decision rules are often hard coded and therefore not very adaptable to different user's actual plans do not correspond to the assistive system's behaviour. Moreover, navigation algorithms are used that take the wheelchair's kinematic and dynamic constraints only approximately into account. Consequently, these robotic wheelchairs may and do fail in executing the very same maneuvers with which elderly and disabled people have problems. In contrast with previous approaches, this paper presents a user-centered architecture for shared wheelchair control. The framework continuously estimates the user's intention explicitly before trying to assist him or her. The actual navigation assistance is performed by a fine motion planner that takes the kinematic and dynamic constraints into account. The paper presents experimental results and an evaluation of the architecture.

The paper discusses an approach to multiagent coordination and control inspired by the behaviors of social insect societies. It enables an agent-based control architecture where the desired control behavior emerges from the single activities of individual agents without exposing them to the complexity and dynamics of the overall system. The research presented is demonstrated in the manufacturing domain. However, the approach is applicable to other types of "ironware" systems.

The paper consists of two main contributions. First, a generic object recognition based on affinely invariant regions is proposed. Next, this algorithm is used in an integrated supervisory control system (ISCS), with special attention to the human machine interaction. Experiments on an industrial robotic system are included, performing simple tasks such as "Go to object O/sub 1/" or "Put object O/sub 2/ on top of object O/sub 3/", where an object is simply modeled by one or more images thereof.

In a holonic shop floor control (SFC) architecture, a reactive scheduler co-operates with an autonomous online SFC system to combine high performance with reactivity against disturbances. To take its decisions, the online SFC system uses the existing schedule as advice. This "schedule execution" (SE) problem is not trivial, because the decisions taken by the online SFC system are local decisions in a combinatorial optimisation problem. By representing the schedule as a graph, this paper presents an algorithm for SE, based on perturbation analysis. It tackles the nonlinearities in the production system by a combination of linearisation and feedback control.

This paper analyses scale effects for actuator types which are strong candidates for micro-actuation: shape memory alloys (SMAs), electrostatic, electromagnetic and piezoelectric actuators. SMA actuators are studied in detail and compared to the other actuator types on a basis of force, response time, and power density. Much of the analysis is based on thermal aspects and scaling effects on both convective and conductive heat transfer, which is a quite novel approach. Results of this thermal analysis are effects on surface temperature and maximum current density of microactuators.

Two new processes, ELID-grinding and high speed chipping, can combine high removal rates with high surface quality through a mechanism of sub-micron chip removal. For the successful application of these processes, ultra-precision positioning systems are needed. Classical solutions, based on traditional guideway technology, cannot meet the requirements of these new machining processes. The piezostepper solution presented in this paper can provide accurate motion, combined with high stiffness, through the use of piezoelectric actuators with high resolution. High passive stiffness is provided by integrating the driving, bearing and transmission functions. The same actuators can provide infinite active stiffness by referring the position of the positioning stage with respect to an absolute reference frame. In contrast with existing (position-controlled) piezo-steppers, this paper presents a way to provide smooth stepping by avoiding hammering through hybrid force-position controlled clamping and by rolling traction.

The choice of "where to look next" is a special case of an optimal experiment design. The paper proposes the tolerance-optimal experiment design, which is a special instance of the well-known L-optimal design, that minimises the weighted trace of the covariance matrix of the estimated state under Gaussian assumptions. The weighting matrix is chosen such that the design is invariant to transformations with nonsingular Jacobians, and such that the emerging sensing sequence reflects the information needs of the task. This tolerance-optimal design does not require more calculations than existing optimal experiment designs. Existing optimal experiment designs do not reflect the information needs of the task. In addition, some of them physically do not make sense if the estimated state has inconsistent units. The tolerance-optimal design is illustrated with two examples.

The presented local world modelling tool assists a human operator of a teleoperated robot in a nuclear environment in locating and recognising stationary objects with an ultrasonic sensor. The interpretation of the measurements of this type of sensor is not trivial for a human operator. In addition, not only the uncertainty on the measurement value is important, but also the uncertainty on the origin of the measurement needs to be dealt with, i.e. from which feature of which object the measurement originates. The Bayesian multiple-hypothesis tree allows different hypotheses on the origin of a measurement to coexist if the ambiguity cannot be resolved at once. For each of these hypotheses, rigorous Bayesian methods are used to update the estimate of the object location, and to calculate its probability. The presented algorithm is easier to apply than other existing multiple hypothesis algorithms. The probability of each local scene model is calculated as the probability of this residual error of the estimate. It is shown that this residual error can be calculated in a trivial way from the results of the Kalman filter based estimator. In addition, the assumption of a static world allows to do without complex probability calculus.

This paper presents the Smoothly Constrained Kalman Filter (SCKF) for nonlinear constraints. A constraint is any relation that exists between the state variables. Constraints can be treated as perfect observations. But, linearization errors can prevent the estimate from converging to the true value. Therefore, the SCKF iteratively applies nonlinear constraints as nearly perfect observations, or, equivalently, weakened constraints. Integration of new measurements is interlaced with these iterations, which reduces linearization errors and, hence, improves convergence compared to other iterative methods. The weakening is achieved by artificially increasing the variance of the nonlinear constraint. The paper explains how to choose the weakening values, and when to start and stop the iterative application of the constraint.

This paper discusses experimental robot identification based on a statistical framework. It presents a new approach toward the design of optimal robot excitation trajectories, and formulates the maximum-likelihood estimation of dynamic robot model parameters. The differences between the new design approach and the existing approaches lie in the parameterization of the excitation trajectory and in the optimization criterion. The excitation trajectory for each joint is a finite Fourier series. This approach guarantees periodic excitation which is advantageous because it allows: 1) time-domain data averaging; 2) estimation of the characteristics of the measurement noise, which is valuable in the case of maximum-likelihood parameter estimation. In addition, the use of finite Fourier series allows calculation of the joint velocities and acceleration in an analytic way from the measured position response, and allows specification of the bandwidth of the excitation trajectories. The optimization criterion is the uncertainty on the estimated parameters or a lower bound for it, instead of the often used condition of the parameter estimation problem. Simulations show that this criterion yields parameter estimates with smaller uncertainty bounds than trajectories optimized according to the classical criterion. Experiments on an industrial robot show that the presented trajectory design and maximum-likelihood parameter estimation approaches complement each other to make a practicable robot identification technique which yields accurate robot models.

This paper presents a recognition and location method for objects, composed of primitives with a simple analytical description, using local sensors such as an ultrasonic and an infra-red sensor. As these sensors only return local data, several measurements are needed to obtain global information. To allow effective exploration, on-line estimation is needed. Estimation is based on a constrained Kalman filter, the constraints defining relations between the primitives in the object model. Linearisation errors are reduced with a careful choice of the parametrisation of the primitives and by using a novel version of the constrained Kalman filter. Special features of the presented approach are the easy extendibility to objects composed of a different number and type of primitives, the fast convergence due to the effective use of geometric constraints, the ability to handle incomplete models, and the robustness to linearisation errors. The performance of the estimator has been thoroughly evaluated with simulations and experiments, showing that 10 measurements with an infrared sensor on the edges of a 800/spl times/550 mm/sup 2/ rectangular plate are sufficient to locate a point on the plate with standard deviation /spl sigma/=3 mm.

In this paper a new mathematically exact algorithm is described for dynamic map building with geometrical primitives for a mobile robot. The dynamic map is built up using a 2D range finder mounted on the mobile robot LiAS which is navigating in the environment. The dynamic map can be used for either planning or localisation purposes. The map is composed of line segments and circles. The parameters describing the geometrical primitives are provided with uncertainties which are used in the matching phase and which are necessary if the map is used for localisation. This paper describes in detail how the uncertainty on the robot position and the uncertainty on a single range measurement leads to the uncertainty on the parameters of a geometrical primitive. Promising experimental results obtained by the algorithm in real unstructured environments are presented.

The authors describe a system that synthesizes robot programs for two-dimensional assemblies. Starting from a high-level problem definition, a number of algorithmic and heuristic modules gradually build a network of augmented compliant motion specification, determining how the robot must execute the successive assembly stages. The system has been implemented on a workstation yielding a satisfactory performance.>

An inverse-dynamics-based method for discrete-time control of flexible-joint robots is presented. The main drawbacks of continuous-time analysis are its computational burden and the necessity for very high sampling frequencies. These inconveniences can be avoided by the use of numerical methods conceived for the solution of systems of differential-algebraic equations. Such an approach naturally leads to a predictive control scheme. As a consequence, in the control process a basic dynamic model of the flexible-joint robot can be used, which is much less complex than in the classical inverse dynamics solution. At the same time, the simplified inverse dynamics approach discussed here accepts low sampling frequencies. Control algorithms are presented and their properties are discussed. Successful experiments on computer control of a two-link manipulator with one flexible joint are described.>

The design of a knowledge-based scheduling system for a flexible assembly cell is discussed. The system integrates disruptions and can be connected to a real-time cell controller. It is composed of a static database, a dynamic database, and expert modules and is driven by an asynchronous clock. The knowledge is hierarchically organized in the distinct expert modules, each standing for a decision level. The scheduler is characterized by a scheduling-in-advance methodology. This capability is to be used on- or offline.>