Aerospace Medicine and Biology. A continuing bibliography (Supplement 226)

This bibliography lists 129 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1981

Haptics in Robot-Assisted Surgery: Challenges and Benefits

Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of man-machine collaborative type, surgical robots are seen as media that transfer pre- and intra-operative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper objectives and challenges of deploying haptic technologies in surgical robotics is discussed and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It has been tried to encompass both classical works and the state of the art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts

Contact force regulation in physical human-machine interaction based on model predictive control

With increasing attention to physical human-machine interaction (pHMI), new control methods involving contact force regulation in collaborative and coexistence scenarios have spread in recent years. Thanks to its internal robustness, high dynamic performance, and capabilities to avoid constraint violations, a Model Predictive Control (MPC) action can pose a viable solution to manage the uncertainties involved in those applications. This paper uses an MPC-driven control method that aims to apply a well-defined and tunable force impulse on a human subject. After describing a general control design suitable to achieve this goal, a practical implementation of such a logic, based on an MPC controller, is shown. In particular, the physical interaction considered is the one occurring between the body of a patient and an external perturbation device in a dynamic posturography trial. The device prototype is presented in both its hardware architecture and software design. The MPC-based main control parameters are thus tuned inside hardware-in-the-loop and human-in-the-loop environments to get optimal behaviors. Finally, the device performance is analyzed to assess the MPC algorithm’s accuracy, repeatability, flexibility, and robustness concerning the several uncertainties due to the specific pHMI environment considered

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

Hybrid Magneto-Rheological Actuators for Human Friendly Robotic Manipulators

In recent years, many developments in the field of the physical human robot interaction (pHRI) have been witnessed and significant attentions have been given to the subject of safety within the interactive environments. Ensuring the safety has led to the design of the robots that are physically unable to hurt humans. However, Such systems commonly suffer from the safety-performance trade-off. Magneto-Rheological (MR) fluids are a special class of fluids that exhibit variable yield stress with respect to an applied magnetic field. Devices developed with such fluids are known to provide the prerequisite requirements of intrinsic safe actuation while maintaining the dynamical performance of the actuator. In this study, a new concept for generating magnetic field in Magneto-Rheological (MR) clutches is presented. The main rationale behind this concept is to divide the magnetic field generation into two parts using an electromagnetic coil and a permanent magnet. The main rationale behind this concept is to utilize a hybrid combination of electromagnetic coil and a permanent magnet. The combination of permanent magnets and electromagnetic coils in Hybrid Magneto-Rheological (HMR) clutches allows to distribute the magnetic field inside an MR clutch more uniformly. Moreover, The use of a permanent magnet dramatically reduces the mass of MR clutches for a given value of the nominal torque that results in developing higher torque-to-mass ratio. High torque-to-mass and torque-to-inertia ratios in HMR clutches promotes the use of these devices in human-friendly actuation

Biomimetic Manipulator Control Design for Bimanual Tasks in the Natural Environment

As robots become more prolific in the human environment, it is important that safe operational procedures are introduced at the same time; typical robot control methods are often very stiff to maintain good positional tracking, but this makes contact (purposeful or accidental) with the robot dangerous. In addition, if robots are to work cooperatively with humans, natural interaction between agents will make tasks easier to perform with less effort and learning time. Stability of the robot is particularly important in this situation, especially as outside forces are likely to affect the manipulator when in a close working environment; for example, a user leaning on the arm, or task-related disturbance at the end-effector. Recent research has discovered the mechanisms of how humans adapt the applied force and impedance during tasks. Studies have been performed to apply this adaptation to robots, with promising results showing an improvement in tracking and effort reduction over other adaptive methods. The basic algorithm is straightforward to implement, and allows the robot to be compliant most of the time and only stiff when required by the task. This allows the robot to work in an environment close to humans, but also suggests that it could create a natural work interaction with a human. In addition, no force sensor is needed, which means the algorithm can be implemented on almost any robot. This work develops a stable control method for bimanual robot tasks, which could also be applied to robot-human interactive tasks. A dynamic model of the Baxter robot is created and verified, which is then used for controller simulations. The biomimetic control algorithm forms the basis of the controller, which is developed into a hybrid control system to improve both task-space and joint-space control when the manipulator is disturbed in the natural environment. Fuzzy systems are implemented to remove the need for repetitive and time consuming parameter tuning, and also allows the controller to actively improve performance during the task. Experimental simulations are performed, and demonstrate how the hybrid task/joint-space controller performs better than either of the component parts under the same conditions. The fuzzy tuning method is then applied to the hybrid controller, which is shown to slightly improve performance as well as automating the gain tuning process. In summary, a novel biomimetic hybrid controller is presented, with a fuzzy mechanism to avoid the gain tuning process, finalised with a demonstration of task-suitability in a bimanual-type situation.EPSR

Model-free Optimization of Trajectory And Impedance Parameters on Exercise Robots With Applications To Human Performance And Rehabilitation

This dissertation focuses on the study and optimization of human training and its physiological effects through the use of advanced exercise machines (AEMs). These machines provide an invaluable contribution to advanced training by combining exercise physiology with technology. Unlike conventional exercise machines (CEMs), AEMs provide controllable trajectories and impedances by using electric motors and control systems. Therefore, they can produce various patterns even in the absence of gravity. Moreover, the ability of the AEMs to target multiple physiological systems makes them the best available option to improve human performance and rehabilitation. During the early stage of the research, the physiological effects produced under training by the manual regulation of the trajectory and impedance parameters of the AEMs were studied. Human dynamics appear as not only complex but also unique and time-varying due to the particular features of each person such as its musculoskeletal distribution, level of fatigue,fitness condition, hydration, etc. However, the possibility of the optimization of the AEM training parameters by using physiological effects was likely, thus the optimization objective started to be formulated. Some previous research suggests that a model-based optimization of advanced training is complicated for real-time environments as a consequence of the high level of v complexity, computational cost, and especially the many unidentifiable parameters. Moreover, a model-based method differs from person to person and it would require periodic updates based on physical and psychological variations in the user. Consequently, we aimed to develop a model-free optimization framework based on the use of Extremum Seeking Control (ESC). ESC is a non-model based controller for real-time optimization which its main advantage over similar controllers is its ability to deal with unknown plants. This framework uses a physiological effect of training as bio-feedback. Three different frameworks were performed for single-variable and multi-variable optimization of trajectory and impedance parameters. Based on the framework, the objective is achieved by seeking the optimal trajectory and/or impedance parameters associated with the orientation of the ellipsoidal path to be tracked by the user and the stiffness property of the resistance by using weighted measures of muscle activations