Heavy tools manipulation by low powered direct-drive five-bar parallel robot

This paper presents a simple and available system for manipulation of heavy tools by low powered manipulator for industrial applications. In the heavy manufacturing industries, sometimes, heavy tools are employed for different types of work. But the application of robots with heavy tools is not possible due to the limited torque limits of actuators. Suspended tool systems (STS) have been proposed to manipulate heavy tools by low powered robot-arm for this purpose. A low powered five-bar direct-drive parallel manipulator is designed and constructed to manipulate heavy tools suspended from a spring balancer. The validity, usefulness, and effectiveness of the suspended tool system are shown by experimental results.

A haptic platform to enable fingertip grasping and manipulation

Haptic technologies allow human users to haptically interact with virtual environments. Haptics has been employed in many application domains including operator training, virtual exploration and teleoperation. Currently, most commercially available haptic devices focus on a single point of haptic interaction. While single-point haptics have been successfully employed in many applications, they remain limited to particular types of haptic interaction. Multi-point haptic devices are a logical progression and facilitate a far wider range of interactions including object grasping, multi-finger object manipulation and size discrimination. The ability to effectively achieve such interactions offers significant benefits for many applications including virtual training, telesurgery and telemanipulation. In such applications, the ability to use multi-point haptic interactions can provide far more effective user interaction as well improved perception of the virtual environment.

Robotic grasping and manipulation controller framework: architecture redevelopment

This paper details the further improvements obtained by redesigning a previously offered Manipulation Controller Framework to provide support to an innovative, friction-based object slippage detection strategy employed by the robotic object manipulator. This upgraded Manipulation Controller Framework includes improved slippage detection functionality and a streamlined architecture designed to improve controller robustness, reliability and speed. Improvements include enhancements to object slippage detection strategy, the removal of the decision making module and integration of its functionality into the Motion Planner, and the stream-lining of the Motion Planner to improve its effectiveness. It is anticipated that this work will be useful to researchers developing integrated robot controller architectures and slippage control.

Grip and load force coordination in cyclical isometric manipulation task is not affected by the feedback type

Background: The relationship between normal and tangential force components (grip force - GF and load force - LF, respectively) acting on the digits-object interface during object manipulation reveals neural mechanisms involved in movement control. Here, we examined whether the feedback type provided to the participants during exertion of LF would influence GF-LF coordination and task performance. Methods. Sixteen young (24.7 ±3.8 years-old) volunteers isometrically exerted continuously sinusoidal FZ (vertical component of LF) by pulling a fixed instrumented handle up and relaxing under two feedback conditions: targeting and tracking. In targeting condition, FZ exertion range was determined by horizontal lines representing the upper (10 N) and lower (1 N) targets, with frequency (0.77 or 1.53 Hz) dictated by a metronome. In tracking condition, a sinusoidal template set at similar frequencies and range was presented and should be superposed by the participants' exerted FZ. Task performance was assessed by absolute errors at peaks (AEPeak) and valleys (AEValley) and GF-LF coordination by GF-LF ratios, maximum cross-correlation coefficients (r max), and time lags. Results: The results revealed no effect of feedback and no feedback by frequency interaction on any variable. AE Peak and GF-LF ratio were higher and rmax lower at 1.53 Hz than at 0.77 Hz. Conclusion: These findings indicate that the type of feedback does not influence task performance and GF-LF coordination. Therefore, we recommend the use of tracking tasks when assessing GF-LF coordination during isometric LF exertion in externally fixed instrumented handles because they are easier to understand and provide additional indices (e.g., RMSE) of voluntary force control. © 2013 Pedão et al.; licensee BioMed Central Ltd.

Mechanical design optimization for multi-finger haptic devices applied to virtual grasping manipulation

This paper describes the design of a modular multi-finger haptic device for virtual object manipulation. Mechanical structures are based on one module per finger and can be scaled up to three fingers. Mechanical configurations for two and three fingers are based on the use of one and two redundant axes, respectively. As demonstrated, redundant axes significantly increase workspace and prevent link collisions, which is their main asset with respect to other multi-finger haptic devices. The location of redundant axes and link dimensions have been optimized in order to guarantee a proper workspace, manipulability, force capability, and inertia for the device. The mechanical haptic device design and a thimble adaptable to different finger sizes have also been developed for virtual object manipulation.

Activation of human primary motor cortex during action observation: A neuromagnetic study

The monkey premotor cortex contains neurons that discharge during action execution and during observation of actions made by others. Transcranial magnetic stimulation experiments suggest that a similar observation/execution matching system also is present in humans. We recorded neuromagnetic oscillatory activity of the human precentral cortex from 10 healthy volunteers while (i) they had no task to perform, (ii) they were manipulating a small object, and (iii) they were observing another individual performing the same task. The left and right median nerves were stimulated alternately (interstimulus interval, 1.5 s) at intensities exceeding motor threshold, and the poststimulus rebound of the rolandic 15- to 25-Hz activity was quantified. In agreement with previous studies, the rebound was strongly suppressed bilaterally during object manipulation. Most interestingly, the rebound also was significantly diminished during action observation (31–46% of the suppression during object manipulation). Control experiments, in which subjects were instructed to observe stationary or moving stimuli, confirmed the specificity of the suppression effect. Because the recorded 15- to 25-Hz activity is known to originate mainly in the precentral motor cortex, we concluded that the human primary motor cortex is activated during observation as well as execution of motor tasks. These findings have implications for a better understanding of the machinery underlying action recognition in humans.

Neuromuscular-skeletal constraints on the acquisition of skill in a discrete torque production task

The organisation of the human neuromuscular-skeletal system allows an extremely wide variety of actions to be performed, often with great dexterity. Adaptations associated with skill acquisition occur at all levels of the neuromuscular-skeletal system although all neural adaptations are inevitably constrained by the organisation of the actuating apparatus (muscles and bones). We quantified the extent to which skill acquisition in an isometric task set is influenced by the mechanical properties of the muscles used to produce the required actions. Initial performance was greatly dependent upon the specific combination of torques required in each variant of the experimental task. Five consecutive days of practice improved the performance to a similar degree across eight actions despite differences in the torques required about the elbow and forearm. The proportional improvement in performance was also similar when the actions were performed at either 20 or 40% of participants' maximum voluntary torque capacity. The skill acquired during practice was successfully extrapolated to variants of the task requiring more torque than that required during practice. We conclude that while the extent to which skill can be acquired in isometric actions is independent of the specific combination of joint torques required for target acquisition, the nature of the kinetic adaptations leading to the performance improvement in isometric actions is influenced by the neural and mechanical properties of the actuating muscles.

Reactive reaching and grasping on a humanoid: Towards closing the action-perception loop on the iCub

We propose a system incorporating a tight integration between computer vision and robot control modules on a complex, high-DOF humanoid robot. Its functionality is showcased by having our iCub humanoid robot pick-up objects from a table in front of it. An important feature is that the system can avoid obstacles - other objects detected in the visual stream - while reaching for the intended target object. Our integration also allows for non-static environments, i.e. the reaching is adapted on-the-fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. Furthermore we show that this system can be used both in autonomous and tele-operation scenarios.

Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.

A modular planar robotic manipulandum with end-point torque control.

Robotic manipulanda are extensively used in investigation of the motor control of human arm movements. They permit the application of translational forces to the arm based on its state and can be used to probe issues ranging from mechanisms of neural control to biomechanics. However, most current designs are optimized for studying either motor learning or stiffness. Even fewer include end-point torque control which is important for the simulation of objects and the study of tool use. Here we describe a modular, general purpose, two-dimensional planar manipulandum (vBOT) primarily optimized for dynamic learning paradigms. It employs a carbon fibre arm arranged as a parallelogram which is driven by motors via timing pulleys. The design minimizes the intrinsic dynamics of the manipulandum without active compensation. A novel variant of the design (WristBOT) can apply torques at the handle using an add-on cable drive mechanism. In a second variant (StiffBOT) a more rigid arm can be substituted and zero backlash belts can be used, making the StiffBOT more suitable for the study of stiffness. The three variants can be used with custom built display rigs, mounting, and air tables. We investigated the performance of the vBOT and its variants in terms of effective end-point mass, viscosity and stiffness. Finally we present an object manipulation task using the WristBOT. This demonstrates that subjects can perceive the orientation of the principal axis of an object based on haptic feedback arising from its rotational dynamics.

Swimming direction reversal of flagella through ciliary motion of mastigonemes.

Bio-inspired designs can provide an answer to engineering problems such as swimming strategies at the micron or nano-scale. Scientists are now designing artificial micro-swimmers that can mimic flagella-powered swimming of micro-organisms. In an application such as lab-on-a-chip in which micro-object manipulation in small flow geometries could be achieved by micro-swimmers, control of the swimming direction becomes an important aspect for retrieval and control of the micro-swimmer. A bio-inspired approach for swimming direction reversal (a flagellum bearing mastigonemes) can be used to design such a system and is being explored in the present work. We analyze the system using a computational framework in which the equations of solid mechanics and fluid dynamics are solved simultaneously. The fluid dynamics of Stokes flow is represented by a 2D Stokeslets approach while the solid mechanics behavior is realized using Euler-Bernoulli beam elements. The working principle of a flagellum bearing mastigonemes can be broken up into two parts: (1) the contribution of the base flagellum and (2) the contribution of mastigonemes, which act like cilia. These contributions are counteractive, and the net motion (velocity and direction) is a superposition of the two. In the present work, we also perform a dimensional analysis to understand the underlying physics associated with the system parameters such as the height of the mastigonemes, the number of mastigonemes, the flagellar wave length and amplitude, the flagellum length, and mastigonemes rigidity. Our results provide fundamental physical insight on the swimming of a flagellum with mastigonemes, and it provides guidelines for the design of artificial flagellar systems.

Context-dependent decay of motor memories during skill acquisition.

Current models of motor learning posit that skill acquisition involves both the formation and decay of multiple motor memories that can be engaged in different contexts. Memory formation is assumed to be context dependent, so that errors most strongly update motor memories associated with the current context. In contrast, memory decay is assumed to be context independent, so that movement in any context leads to uniform decay across all contexts. We demonstrate that for both object manipulation and force-field adaptation, contrary to previous models, memory decay is highly context dependent. We show that the decay of memory associated with a given context is greatest for movements made in that context, with more distant contexts showing markedly reduced decay. Thus, both memory formation and decay are strongest for the current context. We propose that this apparently paradoxical organization provides a mechanism for optimizing performance. While memory decay tends to reduce force output, memory formation can correct for any errors that arise, allowing the motor system to regulate force output so as to both minimize errors and avoid unnecessary energy expenditure. The motor commands for any given context thus result from a balance between memory formation and decay, while memories for other contexts are preserved.

An Encoded Infrared Sheet Of Light Navigational Beacon System For Precise Localization Of Indoor Mobile Robot Vehicles

A new localization approach to increase the navigational capabilities and object manipulation of autonomous mobile robots, based on an encoded infrared sheet of light beacon system, which provides position errors smaller than 0.02m is presented in this paper. To achieve this minimal position error, a resolution enhancement technique has been developed by utilising an inbuilt odometric/optical flow sensor information. This system respects strong low cost constraints by using an innovative assembly for the digitally encoded infrared transmitter. For better guidance of mobile robot vehicles, an online traffic signalling capability is also incorporated. Other added features are its less computational complexity and online localization capability all these without any estimation uncertainty. The constructional details, experimental results and computational methodologies of the system are also described

Perceptual cues for orientation in a two finger haptic grasp task

Single point interaction haptic devices do not provide the natural grasp and manipulations found in the real world, as afforded by multi-fingered haptics. The present study investigates a two-fingered grasp manipulation involving rotation with and without force feedback. There were three visual cue conditions: monocular, binocular and projective lighting. Performance metrics of time and positional accuracy were assessed. The results indicate that adding haptics to an object manipulation task increases the positional accuracy but slightly increases the overall time taken.

Extending the friction cone algorithm for arbitrary polygon based haptic objects

Most haptic environments are based on single point interactions whereas in practice, object manipulation requires multiple contact points between the object, fingers, thumb and palm. The Friction Cone Algorithm was developed specifically to work well in a multi-finger haptic environment where object manipulation would occur. However, the Friction Cone Algorithm has two shortcomings when applied to polygon meshes: there is no means of transitioning polygon boundaries or feeling non-convex edges. In order to overcome these deficiencies, Face Directed Connection Graphs have been developed as well as a robust method for applying friction to non-convex edges. Both these extensions are described herein, as well as the implementation issues associated with them.