A simulation algorithm to predict expected upper limb movements of prosthesis users performing activities of daily living (ADL) was developed. It is quite challenging to determine the right type and fit of a prosthesis and provide appropriate training to properly utilize it. The amputee care team typically uses prior experiences to provide prescription and training customized for each individual. It is also very difficult to anticipate expected and undesired compensatory motions due to reduced degrees of freedom of a prosthesis user. We have developed a tool to predict and visualize the expected upper limb movements resulting from using a prescribed prosthesis and its suitability to the needs of the amputee. It is expected to help clinicians make decisions such as the type of the prosthesis, and whether to include a wrist joint, based on the impact it will have on the rest of the joints. The main focus of this work is to use robotics-based methods to simulate human use of prostheses and identify the expected posture of the limited joints on the upper limbs. Unlike other works, this paper does not discuss the control of the prosthesis but the posture of the body. A weighted least-norm inverse kinematics algorithm was used to develop a robotics-based model of the upper limbs and torso. Motion capture data from the subjects were used to determine the weighting matrix the algorithm required. Results show that this approach provides human-like simulation of joint motions and matches the motion capture data. The algorithm uses the individual's anthropometrics and level of amputation to create a personalized kinematic model of the upper body and the joint motions during ADLs. A graphic user interface (GUI) was created to allow the clinician to input the relevant data resulting in arm movements of the prospective prosthesis user. A custom-made visualization software was developed to display an animation performing the simulated motion. It should be stressed that this work does not adjust and replay motion capture recordings, but solves the inverse kinematics of the human body.

Robotic manipulators that are attached to mobile platforms are often used in workspaces that require the end-effector to mobilize beyond the manipulator's limited reach, such as in warehouse shelf stacking and similar applications. However, such assistive robots fall short of completing tasks that require the end-effector to be situated in a specific configuration at a critical time during the task. Traditionally, users control the mobile base to situate the arm such that the task can be completed through continuous operation. This requires an experienced operator who can predict the needed end-effector workspace, and can operate the base accordingly to maximize the likelihood of a successful task while avoiding any floor obstacles. In this work, we propose a straightforward control method that provides sufficient freedom to the end-effector to complete a task that is bound by time-dependent constraints. This is achieved by relaxing the time constraints on the mobile base trajectory in a floor space obstructed by obstacles. The trajectory of the platform is determined by sensor-assisted obstacle avoidance algorithm such that a single degree of freedom mobility can be represented through a safe obstacle-free time-independent path. The proposed control method is implemented in simulation and on physical hardware built in our labs. The simulation included a 5-DoF redundant Planar Mobile Manipulator (PMM). The hardware implementation and testing utilized a 9-DoF redundant mobile manipulator. The implementation results demonstrate the effectiveness of the control method in adjusting the mobile platform motion along its allowed obstacle-free path to enable the end-effector to follow its trajectory for task completion that would otherwise fail to complete when conventional control methods are used.

The human upper body has many degrees of freedom (DoFs) making it highly redundant and allowing for great flexibility during daily tasks. Because of this redundancy, each joint rarely uses its full range of motion (ROM) during activities of daily living (ADL). However, when DoFs are lost due to an amputation, a greater ROM of the intact joints is used. This decreases the number of achievable postures. This paper describes, a kinematic robotic human body model (RHBM) that is used to simulate optimum upper body movements of able-bodied and transradial prosthesis users. The motions of a total of 22 subjects from these two groups were evaluated during five ADLs. The RHBM uses a weighted-least-norm (WLN) solution to bias each joint resulting in a human-like posture. The weights were dynamically updated to prevent the joints from moving beyond the individual's task specific joint limits. These limits were determined by the recorded data of each participant. Applying the individualized joint limits, resulted in postures that were always physically possible for each person and more accurate than the static WLN solution. In the case of prosthesis users, the static WLN solution tended to predict joint angles that were outside of the physical joint limits. The inclusion of joint limit avoidance helped predict more human-like postures.

In this work, we present a methodology for enabling a robot to identify an object and grasp configuration of interest and assist the human teleoperating the robot, to grasp the object. The identification is carried out in real-time by detecting the motion intention of the human as they are teleoperating the remote robotic arm towards the object and the grasp configuration. Simultaneously, depending on the detected object and grasp configuration, the human user is assisted to translate and orient the remote arm gripper in order to preshape and grasp the object. The complete process occurs with the human teleoperating the arm, and without them having to interact with another interface. Motion intention recognition is carried out by using Hidden Markov Models (HMMs), trained offline by preshape trials performed by a skilled teleoperator. The environment is unstructured and comprises of a number of objects, each with multiple grasp configurations. Experimental tests on healthy human subjects have validated our intention recognition based assistance method. They show that the method allows objects to be grasped and placed 48% faster, and with much ease compared to unassisted teleoperation. Moreover, we have proved that the model for intention recognition, trained by a skilled teleoperator, can be used by novice users to efficiently execute a grasping task in teleoperation.

Rapidly acquiring the shape and pose information of unknown objects is an essential characteristic of modern robotic systems in order to perform efficient manipulation tasks. In this work, we present a framework for 3D geometric shape recovery and pose estimation from unorganized point cloud data. We propose a low latency multi-scale voxelization strategy that rapidly fits superquadrics to single view 3D point clouds. As a result, we are able to quickly and accurately estimate the shape and pose parameters of relevant objects in a scene. We evaluate our approach on two datasets of common household objects collected using Microsoft's Kinect sensor. We also compare our work to the state of the art and achieve comparable results in less computational time. Our experimental results demonstrate the efficacy of our approach.

Accurately predicting the movements of the human upper body is an obstacle in simulating human movement. This paper describes the optimization and comparison of three inverse kinematic algorithms designed to predict the pose of a 25 degree of freedom robotic human upper body model (RHBM). Motion analysis data of 10 subjects performing 5 activities of daily living were used to evaluate the performance of each method. The first algorithm used a numerically optimized weighted-least-norm (WLN) solution. The second algorithm maximized the joint angle probability density, using the gradient projection method (GP). The third algorithm used a single layer artificial neural network (NN), trained by Levenberg-Marquart backpropagation using the motion analysis data. Error was evaluated using the root mean square of the difference between calculated and recorded joint angles. The robustness was then tested by progressively excluding subject data from the training set, re-training the algorithms, and evaluating the error for all subjects. The numerically optimized WLN solution showed the highest robustness, and the GP and NN solutions had greater accuracy for the data included in training and lower accuracy for the data excluded from training. The gradient projection method showed greater robustness than the artificial neural network, and has potential to be refined and combined with the weighted least norm solution to increase accuracy and robustness. Future work will investigate combined methods and the ability to predict motion of persons using prostheses.

The wheelchair-mounted robotic arm (WMRA) is a mobile manipulator that consists of a 7-DoF robotic arm and a 2-DoF power wheelchair platform. Previous works combined mobility and manipulation control using weighted optimization for dual-trajectory tracking [7]. In this work, we present an image-based visual servoing (IBVS) approach with scale-invariant feature transform (SIFT) using an eye-in-hand monocular camera for combined control of mobility and manipulation for the 9-DoF WMRA system to execute activities of daily living (ADL) autonomously. We also present results of the physical implementation with a simple “Go to and Pick Up” task and the “Go to and Open the Door” task previously published in simulation, using IBVS to aid the task performance.

Commercially available wheelchair-mounted robotic arms (WMRAs) are becoming more prevalent internationally but have yet to be largely developed and approved by the Food and Drug Administration in the United States. The purpose of this study was to perform kinematic and experimental analysis of commercially available WMRAs. The goal was to quantitatively compare each device through a standardized testing protocol. The study and its procedures may serve as a source of quantitative evaluation data for commercially available and developmental WMRAs.

Wheelchair-bound persons with upper limb motion limitations can utilize a wheelchair-mounted robotic arm (WMRA) to perform activities of daily living (ADL) tasks. In this paper, an optimized control of our 9-DoF system, consisting of a 7-DoF robotic arm and a 2-DoF power wheelchair, is achieved. For effective ADL task execution, positioning the end-effector with proper wheelchair orientation was optimized as part of the control algorithm. Separate wheelchair and end-effector trajectories were simultaneously followed to execute a “Go To and Open the Door” task. The control methodology, implementation and test results in simulation are presented in this paper.

When planning robotic grasping and manipulation maneuvers, knowledge of the shape and pose of the object of interest is critical information. In order for an autonomous or semi-autonomous system to operate intelligently in an unstructured environment and interact with novel objects, it must have the ability to recover this information at run time, even when no a priori information of the object is available. In this paper, we describe the development and testing of an algorithm that can reconstruct the full 3D geometry of a novel object from just three images. A variant of shape from silhouettes, the algorithm first generates a rough surface approximation in the form of a point cloud. This approximation is then refined by fitting an eleven parameter geometric surface to the points in such a manner that the surface ignores noise and perspective projection shadows. We test the algorithm in both simulation and on several real world objects. We show that the algorithm provides accurate reconstructions that can be directly used to plan grasping maneuvers. Compared to other attempts in the literature, the proposed algorithm is faster, requires fewer images, is more accurate, and degrades gracefully in the presence of bad data. A real world test case is included that shows that the algorithm still yields usable results when the form of the object is amorphous or otherwise non-geometric.

In this paper, we demonstrate the use of range information from a laser sensor mounted on the end-effector of a remote robot manipulator to assist persons with limited upper body strength carry out Activities of Daily Living in unstructured environments. Laser range data is used to determine goals, identify targets, obstacles, and via points to enable autonomous execution of trajectories. The human operator is primarily involved in higher level decision making; the human user performs minimal teleoperation to identify critical points in the workspace with the laser pointer. Tests on ten healthy human subjects in executing a pick-and-place task showed that laser-based assistance not only increased the speed of task execution by an average of 26.9%while decreasing the physical effort by an average of 85.4%, but also made the task cognitively easier for the user to execute.

In order to perform activities of daily living (ADL), a person with an amputation(s) must use a greater than normal range of movement from other anatomical body joints to compensate for the loss of movement caused by the amputation, this is called compensatory motion. By studying the compensatory motion of prosthetic users the mechanics of how they adapt to the loss of range of motion in a given limb for can be analyzed for select tasks. The purpose of this study is to create a robotic based kinematic model that can simulate the compensatory motion of a given task using given subject data. This paper reviews the use of the model to simulate compensatory motion of a transradial amputee performing two bilateral tasks: turning a steering wheel, and lifting a box. The simulation operates by changing a set of prosthetic configurations that are represented by parameters that consist of the joint degrees of freedom (DoF) provided by each prosthesis in the set. The task information is inputted into the model by defining a trajectory which the hand or prosthesis must follow to perform the task. The inclusion of the ability to model bilateral tasks is accomplished by giving control of the proximal joints to the prosthetic side. Analysis of tasks is completed by running the simulation with prosthetic and anatomical constraints attached to the left arm of the model, the right arm maintains an anatomical configuration. By running the model through this simulation the compensatory motions can be determined. Results obtained from the model can be used to select the best prosthesis for a given user, design prostheses that are more effective at selected tasks, further analyze previous studies, or to determine areas of interest for further human study.

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9-DoF system combines the 7-DoF robotic arm control with the 2-DoF power wheelchair control. The 3-degrees of redundancy can be optimized to effectively perform activities of daily living (ADLs) and overcome some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, and it offers expandability for future research, such as voice or sip and puff control operations and sensor assist functions

A wheelchair-mounted robotic arm (WMRA) was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The mechanical design incorporates DC servo drive, with actuator hardware at each individual joint, allowing reconfigurable link lengths. It has seven degrees of freedom and uses a side mount on a power wheelchair. The control system allows coordinated Cartesian control, and offers expandability for future research, such as coordinated motion with the wheelchair itself. This paper discusses the current state of the art in WMRAs; describes the design goals and user requirements for this device; explains the component selection process; discusses details of the mechanical design, electrical system and low-level controller; covers manufacturing concerns; and describes the testing of the completed arm. Further improvements are also suggested

A wheelchair-mounted robotic arm (WMRA) was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The mechanical design incorporates DC servo drive, with actuator hardware at each individual joint, allowing reconfigurable link lengths. It has seven degrees of freedom and uses a side mount on a power wheelchair. The control system is built and designed for user-driven or autonomous coordinated Cartesian control, and it offers expandability for future research, such as coordinated motion with the wheelchair itself. This paper discusses the current state of the art in WMRAs; describes the design goals and user requirements for this device; explains the component selection and the mechanical/electrical design; describes the controller design of the arm and optimized redundancy resolution for the Cartesian control. Further improvements are also suggested

In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user’s motion intention. In this paper, user’s motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the end-effector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user’s motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.

We discuss the possibility of improving eyehand coordination in children diagnosed with this problem, using a robotic mapping from a haptic user interface to a virtual environment. Our goal is to develop an assessment and training procedure that will result in improving handwriting taking the advantage of the force feedback provided by the haptic device. Force feedback can be used to guide the subject's hand in a predetermined trajectory when he/she is unable to move in response to visual feedback. We also incorporate the inertia and viscosity effects to decrease the tremor in the hand as well as to stimulate the muscles involved in the task of holding a pencil (known as facilitation technique in the occupational therapy field). The robotic system is operated within ranges established by baseline data for accuracy of eye-hand coordination and grip strength. A set of assessment tests, commonly used by occupational therapists, were chosen to implement the various functions using force, inertia and viscosity effects. The test bed used for these tasks consisted of a six-degree-of-freedom force reflecting haptic interface device, PHANToM with the GHOST SDK Software. Preliminary data obtained from two children diagnosed with eye-hand coordination problems show that the assistance provided, improved their ability to execute and complete the tasks, suggesting that the system could be used for assessment and training purposes. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit and measures the impact on the occupation of handwriting. Furthermore, a real application of this system could be implemented in the future, by identifying specific job tasks that may be made accessible through a desktop in classrooms by the aforementioned haptic system.

This paper describes the Hidden Markov Model (HMM) based skill learning and its application in a motion therapy system using a haptic interface. A relatively complex task, requiring motion along a labyrinth is used. A normal subject executes this task for a number of times and the best trajectory is selected as the learned skill, which is considered as a virtual therapist who can train persons with disabilities to complete the task. Two persons with disabilities on upper limb (cerebral palsy) were trained using the virtual therapist. The performance before and after therapy training, including the smoothness of the trajectory, distance ratio, time taken, tremor and impact forces are presented in this paper. This labyrinth can be used as a physical therapy for upper limb coordination, tremor reduction and motion control capabilities.

In telemanipulation systems, assistance of virtual fixture can improve manipulation capability and dexterity. This assistance provides aids not only for path following, but also for reaching target and avoiding obstacles. Conventionally, these assistances are based on the environment information, without knowing user's motion intention. In this paper, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a hard virtual fixture orthogonal to the path is applied. Or if the task is to position a target, an attractive force filed is produced. In order to successfully recognize user's motion intention, a hidden Markov model (HMM) is developed to classify human actions, such as following path, positioning target and avoiding obstacles. The algorithm is tested on the simulation platform.

This paper describes the development of intelligent mapping from a haptic user interface to a remote manipulator to assist individuals with disabilities performing occupational therapy tasks. This mapping, referred to an assistance function, is determined on the basis of environmental model or sensory data to guide the motion of a telerobotic manipulator while performing a given task. Human input is enhanced rather than superseded by the computer. Three manual dexterity assessment tests, commonly used in the occupational therapy field, were chosen to implement the several forms of assistance functions designed to augment the human performance. The test bed used for these tasks consisted of a six-degree-of-freedom force-reflecting haptic interface device, PHANToM with the GHOST SDK software. This work is the continuation of the authors' previous work at the ICRA'02 [Pernalete, N, et al, 2002], in which only results of fully able individuals were presented. Two motion-impaired users with severe forms of muscular dystrophy and spinal cord injure, volunteered to participate in the experiments and their results are presented in this article. The results demonstrated that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks for the disabled individuals as well as they did for the fully-able persons. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit to motion-impaired users by augmenting their performance on job-related tasks.

This paper describes the development of intelligent mapping from a haptic user interface to a remote manipulator to assist individuals with disabilities performing vocational tasks. This mapping, referred to an assistance function, is determined on the basis of environmental model or sensory data to guide the motion of a telerobotic manipulator while performing a given task. Human input is enhanced rather than superseded by the computer. Three manual dexterity assessment tests, commonly used in the occupational therapy field, were chosen to implement the several forms of assistance functions designed to augment the human performance. The test bed used for these tasks consisted of a six-degree-of-freedom force-reflecting haptic interface device, PHANToM with the GHOST SDK software. The results demonstrate that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit to motion-impaired users by augmenting their performance on job-related tasks.

This paper describes several assistance approaches to use complex and variable telerobotic mapping, such that limited and irregular inputs from an individual with disabilities are mapped into complex motions. The approach presented implies several forms of assistance functions in which the human input is enhanced rather than superseded by the computer. Some complex tasks in the slave manipulator can be implemented through simple and restricted movements on the master's side coming from a person with restricted dexterity. Three different tasks were chosen to implement the assistance functions designed to augment the teleoperation performance of motion-impaired users. A comparison is made between the implementation of the constraints on the master and the slave sides of the system. The system used consisted of the Phantom haptic device and the RRC (Robotics Research Corporation) seven-degree-of-freedom manipulator. The results demonstrated that the forms of assistance provided not only allowed users to perform complex tasks, but also, reduced the execution times and increased the operation accuracy of the chosen tasks.

This paper presents a new approach to the driving assessment of persons with disabilities in which an advanced vehicle interface system is introduced. This system combines a six-degree-of-freedom force reflecting haptic device and a commercially available vehicle modification system. Innovative ergonomic tasks are presented to determine the appropriate position and orientation of the required driving input device. Further, model based computer assistance is incorporated by using assistance functions such as scaling and tremor filtering. Results, obtained from testing a subject with Muscular Dystrophy (MD), demonstrated that the quantitative ergonomic measurements obtained successfully determined the appropriate position and orientation of the modified steering wheel device.

Radioactive tank waste remediation and decontamination and decommissioning (D&D) of contaminated Department of Energy (DOE) facilities, and other nuclear cleanup tasks require extensive remote handling technologies. The unstructured nature of these tasks and limitations of the current sensor and computer decision-making technologies prohibit the use of completely autonomous systems for remote manipulation. This paper presents a new methodology in which model-based computer assistance is incorporated into human controlled teleoperator systems. This approach implies a form of assistance function in which the human input is enhanced rather than superseded by the computer. A specific task of cutting a pipe with a saw is chosen as an example to demonstrate the implementation of the assistance functions in D&D size reduction tasks and the results are presented.

There has been increasing attention on human-machine cooperative teleoperation due to limited capabilities of autonomous robots. A method of sensor and model assisted teleoperation using variable velocity mapping is presented. The application of variable velocity mapping to the execution of Fitts tasks is described, and comparisons are made with traditional teleoperation. Application to a practical task of docking is also described.

Inadequacies in sensor and image processing technology have limited the capabilities of autonomous robotics in complex environments, while tedium and fatigue are commonly encountered when purely manual teleoperation is used. Therefore, attention has been increasingly focused on the integration of human and machine control for remote operations. A previously introduced method of assisted teleoperation, consisting of the variable mapping of velocity commands from the master to slave manipulator using uncertain environmental data, is briefly reviewed. The new method is adapted for the task of maneuvering a manipulator within an enclosed cylindrical environment. A model is used to create a mapping from master to slave workspaces to allow the operator to move around the curved surface easily while avoiding unintended impacts with the wall. Experiments were developed which required fine positioning of the end-effector near the wall, and the results demonstrated the increase in efficiency of task completion and reduction of operator fatigue and skill requirements.

The limitations in the accuracy of sensing and image processing technology have prohibited purely autonomous modes of task execution in complex environments, while manual operation can be tedious and require skilled operators. A recently developed method of combining human and machine control to provide assistance to the operator by variably mapping velocity commands using uncertain sensor data is briefly reviewed. The new concept is used with processed visual information from an end-effector camera to provide assistance in alignment of the end-effector with an object to be grasped. Commanded velocities parallel to the image plane are variably mapped depending on the slave position and orientation and camera image. An experiment using a cross-shaped target was conducted, and results demonstrate the increase in efficiency of task completion and reduction of operator fatigue and skill requirements.

The promise of automatic and efficient remote operations has fallen short of expectations due to the limited ability of computers to reason and plan in unstructured environments and inadequate sensor accuracy. Our approach is to use available but incomplete and imperfect sensor and model data to augment the operator's motions, while the operator retains direct control of the manipulator. The amount and type of modification depends not only on the task but also on the accuracy of environmental information from the sensors or model. This form of machine assistance is accomplished by adjusting system parameters which are not under direct control by the operator, specifically, the mapping of positions and velocities between the master and slave. Experimental results from the application of this strategy in an impact task are presented.

Inadequacies in sensor and image processing technology have limited the capabilities of autonomous robotics in complex environments. However, the tedium and fatigue commonly encountered in manual teleoperation has prompted research in the area of sensor and computer assisted teleoperation. A method of sensor assisted teleoperation consisting of automatic selection of variable velocity mapping parameters based on sensor accuracy is briefly reviewed. The application of variable velocity mapping to the execution of Fitts task is described, and the theoretically predicted increase in performance compared with the actual increases shown. The experimental results are used to support a method of choosing mapping parameters to provide the optimal assistance when there are known inaccuracies in the available sensor data according to the initially described concept.

Efficient telerobotic operation requires an intuitive transmission of forces and velocities between the master and slave manipulators. However, due to friction in the joints of each kinematic chain, the associated Cartesian space impedance matrices are not diagonal. The result is a misalignment between the applied force and resulting velocity vectors. While a force sensor on the slave is often used to provide an internal feedback to eliminate the problem for this manipulator, the same is not usually true for the master. A local feedback of available velocity and force sensory information to the master is proposed which would diagonalize its effective mechanical damping to allow a correct display of force direction information from the slave.

Waste tank cleanup is a current application area for robotics in the United States. As a result of tank access restrictions, designs for several current waste cleanup manipulator systems have long links with small cross-sections and variable lengths. In addition, as a result of anticipated payload capacity, these manipulator designs are hydraulically actuated. This paper takes two approaches to investigating the problem of the control of manipulators having variable-length flexible links and hydraulic actuators. First, a simulation of a hydraulically actuated, 2-DOF, polar (r-/spl Theta/), manipulator with one flexible link is used to illustrate the problem of applying controllers successfully developed for hydraulically actuated, fixed-length flexible manipulators to hydraulically-actuated, variable-length systems. Next, based upon the simulation results, simple controller modifications are proposed and are shown to work on a full-scale experimental test bed. Motion control of hydraulically actuated, variable-length flexible-link manipulators is the focus of this paper.

Stiffness and damping control in a telerobotic system allows the master and slave dynamics to be defined by the user. However, many requirements for good teleoperation produce conflicting choices of desired dynamic parameters. Rather than selecting a compromise value for the damping of the master and slave, it is proposed that these values be functionally dependent on sensed and commanded values of force and velocity, with no previous knowledge of the environment required. A strategy is proposed which provides better quality teleoperation under a variety of circumstances than is achievable with constant dynamics. It is shown that tracking in free space and along a surface by the slave can be maintained while impact forces are minimized. The algorithms have been implemented on a 7-DOF Robotics Research Corporation manipulator with a 6-DOF Kraft master hand controller, and the impact and tracking performance is illustrated.

It is proposed to use weighted least-norm solution to avoid joint limits for redundant joint manipulators. A comparison is made with the gradient projection method for avoiding joint limits. While the gradient projection method provides the optimal direction for the joint velocity vector within the null space, its magnitude is not unique and is adjusted by a scalar coefficient chosen by trial and error. It is shown in this paper that one fixed value of the scalar coefficient is not suitable even in a small workspace. The proposed manipulation scheme automatically chooses an appropriate magnitude of the self-motion throughout the workspace. This scheme, unlike the gradient projection method, guarantees joint limit avoidance, and also minimizes unnecessary self-motion. It was implemented and tested for real-time control of a seven-degree-of-freedom (7-DOF) Robotics Research Corporation (RRC) manipulator.>

Generalized bilateral control in teleoperation enables scaling, indexing, impedance control, shared control and the ability to use a non-replica master slave system. In this paper, control laws for both master and slave are derived for a desired impedance of the teleoperator system. A teleoperator system with six-degree-of-freedom Kraft hand controller (master) and a seven-degree-of-freedom Robotics Research Corporation manipulator (slave), is used as a testbed for generalized bilateral control. Parameter selection guidelines for the controller design are developed based on experimental studies of the teleoperator system. An assembly task is performed to test the effectiveness of the controller design.>

A method to simultaneously optimize multiple performance criteria for a redundant manipulator is presented. Preferred formulation requirements for the criteria are discussed, and previously proposed normalization methods are examined. A statistically based variable weighting technique is developed and evaluated. A seven-degree-of freedom Robotics Research Corporation manipulator is used as a test bed for the multiple criteria optimization scheme.>

Generalized bilateral control in teleoperation enables scaling, indexing, impedance control, shared control and the ability to use a non-replica master slave system. In this paper, control laws for both master and slave are derived for a desired impedance of the teleoperator system. A teleoperator system with six-degree-of-freedom Kraft hand controller (master) and a seven-degree-of-freedom Robotics Research Cooperation manipulator (slave), is used as a test-bed for generalized bilateral control. Parameter selection guidelines for controller design are developed based on experimental studies of the teleoperator system. An assembly task is performed to test the effectiveness of the controller design.>

A computationally efficient kinematic optimal control scheme for seven-degree-of-freedom (7-DOF) manipulators is presented. This scheme uses the gradient projection optimization method in the framework of resolved motion rate control and does not require calculation of the pseudoinverse of the Jacobian. An efficient formulation for determining joint velocities for given Cartesian components of linear and angular end-effector velocities is obtained. This control scheme is well suited for real-time implementation, which is essential if the end-effector trajectory is continuously modified based on sensory feedback. Implementation of this scheme on a Motorola 6820 VME bus-based controller of the 7-DOF manipulator is described. A comparison of computational complexity with previously available schemes is presented.>

An efficient gradient projection optimization scheme is presented for manipulators with multiple degrees of redundancy. This is an extension of the gradient projection scheme presented by R.B. Dubey et al. (Proc. IEEE Int. Conf. on Robotics and Automation, p.28-36, 1989) for manipulators with one degree of redundancy. The feasibility and effectiveness of this scheme are tested through simulations of the seven-degree-of-freedom NASA Laboratory Telerobotic Manipulator (LTM). Only the position of the LTM end effector is controlled, which requires only three degrees of freedom. The remaining joints, except for the wrist roll joint, which does not contribute to the end-effector linear velocity, provide three degrees of redundancy.>

A comparison of two real-time control schemes for redundant manipulators is presented. Both schemes are developed in the framework of resolved rate control, and were presented by the authors in earlier articles (R.V. Dubey et al., 1988, J.A. Euler et al., 1988, and S.M. Babcock, 1988). The first scheme is the gradient projection scheme, which determines only the direction of the self-motion for the optimization of a scalar performance criterion. The second scheme determines the direction as well as the magnitude of the self motion. However, when the magnitude of the self-motion is limited by the hardware bounds on joint velocities, the results obtained using the two schemes are remarkably similar when the computational frequency is high (above 100 Hz). Therefore, the gradient projection scheme, which is computationally more efficient, is recommended. An explanation for the similarity of results is presented. Scheme 1 was implemented in real-time on a Motorola 68020 VME bus-based controller of the seven-degree-of-freedom manipulator at the Center for Engineering Systems Advanced Research.>

A computationally effective kinematic control scheme is presented for a seven-degree-of-freedom redundant robot with spherical wrist. This scheme uses a gradient projection optimization method, which eliminates the need to determine the generalized inverse of the Jacobian when solving for joint velocities for given Cartesian end-effector velocities. Closed-form solutions are obtained for joint velocities using this approach. The application of this scheme to the seven-degree-of-freedom manipulator at the Center for Engineering Systems Advanced Research (CESAR) is described.>

The joint velocities required to move the robot end-effector with a desired speed depend on the direction of motion. Robot's mobility, i.e., its ability to move, is better in the directions requiring lower joint velocities. When the robot is near a singularity configuration, the joint velocities required to attain the end-effector velocity in certain directions are extremely high. Thus arbitrary directional changes become more difficult. Robot's flexibility, defined as its ability to change the direction of the end-effector motion, is low in the vicinity of singular configurations. Addition of redundant joints can greatly enhance their flexibility. However, this requires a proper utilization of redundancy. A control scheme is presented to improve the flexibility of redundant robots. The feasibility and effectiveness of this control scheme are demonstrated through simulation.