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Human motion recognition is essential for many biomedical applications, but few studies compare the abilities of multiple sensing modalities. This paper thus evaluates the effectiveness of different modalities when predicting targets of human reaching movements. Electroencephalography, electrooculography, camera-based eye tracking, electromyography, hand tracking and the user’s preferences are used to make predictions at different points in time. Prediction accuracies are calculated based on data from 10 subjects in within-subject crossvalidation. Results show that electroencephalography can make predictions before limb motion onset, but its accuracy decreases as the number of potential targets increases. Electromyography and hand tracking give high accuracy, but only after motion onset. Eye tracking is robust and gives high accuracy at limb motion onset. Combining multiple modalities can increase accuracy, though not always. While many studies have evaluated individual sensing modalities, this study provides quantitative data on many modalities at different points of time in a single setting. The information could help biomedical engineers choose the most appropriate equipment for a particular application.
Posted on: October 3, 2013
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Mechanical impedance modulation is the key to natural, stable and efficient human locomotion. An improved understanding of this mechanism is necessary for the development of the next generation of intelligent prosthetic and orthotic devices. This paper documents the design methodologies that were employed to realize a knee perturbator that can experimentally estimate human knee impedance during gait through the application of angular velocity perturbations. The proposed experiment requires a light, transparent, wearable, and remotely actuated device that closely follows the movement of the biological joint. A genetic algorithm was used to design a polycentric hinge whose instantaneous center of rotation is optimized to be kinematically compatible with the human knee. A wafer disc clutch was designed to switch between a high transparency passive mode and a high impedance actuated mode. A remote actuation and transmission scheme was designed to enable high power output perturbations while minimizing the device’s mass. Position and torque sensors were designed for device control and to provide data for post-processing and joint impedance estimation. Pending the fabrication and mechanical testing of the device, we expect this knee perturbator to be a valuable tool for experimental investigation of locomotive joint impedance modulation.
Posted on: October 2, 2013
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Drawing inspiration from nature, this paper introduces and compares two compliant robotic legs that are able to perform precise joint torque and position control, enable passive adaption to the environment, and allowfor the exploitation of natural dynamic motions.We report in detail on the design and control of both prototypes and elaborate specifically on the problem of precise foot placement during flight without the sacrifice of efficient energy storage during stance. This is achieved through an integrated design and control approach that incorporates series elastic actuation, series damping actuation, and active damping through torque control. The two legs are employed in efficient hopping/ running motions for which they achieve performance similar to humans or animals. This paper is concluded by a comparison of the various design choices with respect to performance and applicability, as well as an outlook on the usage of these legs in a fully actuated quadruped.
Posted on: October 2, 2013
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Posted on: September 12, 2013
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Manipulation and transport of objects using mobile robotic platforms is a well studied field with several successful approaches. The main difficulty while using such platforms is the lack of adaptation capabilities to changes in the environment and the restriction to flat working areas. In this paper, we present a novel manipulation and transport framework using the self-reconfigurable modular robots Roombots to collaboratively carry arbitrarily shaped passive elements in a non-regular 3D environment equipped with passive connectors. A hierarchical planner based on the notion of virtual kinematic chain is used to generate collision-free and hardware-friendly paths as well as sequences of collaborative manipulations. To the best of our knowledge, this is the first example of manipulation of fully passive elements in an arbitrary 3D environment using mobile self-reconfigurable robots. The simulated results show that the planner is robust to arbitrary complex environments with randomly distributed connectors. In addition to simulation results, a proof of concept of the manipulation of one passive element with two real Roombots meta-modules is described.
Posted on: September 10, 2013
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Posted on: September 9, 2013
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The use of free vibration in elastic structure can lead to energy efficient robot locomotion, since it significantly reduces the energy expenditure if properly designed and controlled. However, it is not well understood how to harness the dynamics of free vibration for the robot locomotion, because of the complex dynamics originated in discrete events and energy dissipation during locomotion. From this perspective, the goal of this paper is to propose a design strategy of hopping robot based on elastic curved beams and actuated rotating masses, and identify the minimalistic model that can characterize the basic principle of robot locomotion. Since the robot mainly exhibits vertical hopping, three one-dimensional models are examined that contain different configurations of simple spring-damper-mass components. The real-world and simulation experiments show that one of the models best characterizes the robot hopping, through analyzing the basic kinematics and negative works in actuation. Based on this model, the self-stability of hopping motion under disturbances is investigated and design and control parameters are analyzed for the energy efficient hopping. Additionally, further analyses show that this robot can achieve the energy efficient hopping with the variation in payload, and the source of energy dissipation of the robot hopping is investigated.
Posted on: September 9, 2013
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In everyday life, people use a large diversity of hands configurations while reaching out to grasp an object. They tend to vary their hands position/orientation around the object and their fingers placement on its surface according to the object properties such as its weight, shape, friction coefficient and the task they need to accomplish. Taking into account these properties, we propose a method for generating such a variety of good grasps that can be used for the accomplishment of many different tasks. Grasp synthesis is formulated as a single constrained optimization problem, generating grasps that are feasible for the hand’s kinematics by minimizing the norm of the joint torque vector of the hand ensuring grasp stability. Given an object and a kinematic hand model, this method can easily be used to build a library of the corresponding object possible grasps. We show that the approach is adapted to different representations of the object surface and different hand kinematic models.
Posted on: August 21, 2013
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This paper addresses the problem of adequately protecting flying robots from damage resulting from collisions that may occur when exploring constrained and cluttered environments. A method for designing protective structures to meet the specific constraints of flying systems is presented and applied to the protection of a small coaxial hovering platform. Protective structures in the form of Euler springs in a tetrahedral configuration are designed and optimised to elastically absorb the energy of an impact while simultaneously minimizing the forces acting on the robot’s stiff inner frame. These protective structures are integrated into a 282 g hovering platform and shown to consistently withstand dozens of collisions undamaged.
Posted on: July 31, 2013
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We consider the problem of incrementally learning different strategies of performing a complex sequential task from multiple demonstrations of an expert or a set of experts. While the task is the same, each expert differs in his/her way of performing it. We assume that this variety across experts’ demonstration is due to the fact that each expert/strategy is driven by a different reward function, where reward function is expressed as a linear combination of a set of known features. Consequently, we can learn all the expert strategies by forming a convex set of optimal deterministic policies, from which one can match any unseen expert strategy drawn from this set. Instead of learning from scratch every optimal policy in this set, the learner transfers knowledge from the set of learned policies to bootstrap its search for new optimal policy. We demonstrate our approach on a simulated mini-golf task where the 7 degrees of freedom Barrett WAM robot arm learns to sequentially putt on different holes in accordance with the playing strategies of the expert.
Posted on: July 23, 2013
