Virtual and rapid prototyping of an underactuated space end effector

A fast and reliable verification of an initial concept is an important need in the field of mechatronics. Usually, the steps for a successful design require multiple iterations involving a sequence of design phases-the initial one and several improvements-and the tests of the resulting prototypes, in a trial and error scheme. Now a day’s software and hardware tools allow for a faster approach, in which the iterations between design and prototyping are by far reduced, even to just one in favorable situation. This work presents the design, manufacturing and testing of a robotic end effector for space applications, realized through virtual prototyping, followed by rapid prototyping realization. The first process allows realizing a mathematical model of the robotic system that, once all the simulations confirm the effectiveness of the design, can be directly used for the rapid prototyping by means of 3D printing. The workflow and the results of the process are described in detail in this paper, showing the qualitative and quantitative evaluation of the performance of both the virtual end effector and the actual physical robotic hand

Dynamics, control and sensor issues pertinent to robotic hands for the EVA retriever system

Basic dynamics, sensor, control, and related artificial intelligence issues pertinent to smart robotic hands for the Extra Vehicular Activity (EVA) Retriever system are summarized and discussed. These smart hands are to be used as end effectors on arms attached to manned maneuvering units (MMU). The Retriever robotic systems comprised of MMU, arm and smart hands, are being developed to aid crewmen in the performance of routine EVA tasks including tool and object retrieval. The ultimate goal is to enhance the effectiveness of EVA crewmen

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration

Grasping Strategies for a Dexterous Hand during Teleoperation

Telerobotics is an interdisciplinary branch of engineering that deals with the control of robots at a distance in a manner that entails the intuition and the physical involvement of the operator controlling the robot. The end of the robotic manipulator consists of a device called an end effector that is used to hold the tools. Most telerobotic systems employ a simple single degree of freedom end effector called a parallel jaw gripper. Since such end effectors have just one degree of freedom and hence limited dexterity, it is essential to develop special fixtures to be attached to the tool that is grasped. The current research attempts to employ a multi fingered end effector, which has multiple degrees of freedom in an attempt to reduce tool fixturing costs and ensure ease of operation. The research integrates the end effector into an existing telerobotic system, develops and implements grasping strategies based on human grasp observations and experimental grasp by demonstration validation for specific tool and objects in an attempt to find stable grasps. The strategies developed are further implemented by designing a master controller for the end effector and integrating it with a human machine interface and the overall system

DEVELOPMENT OF A SOFT PNEUMATIC ACTUATOR FOR MODULAR ROBOTIC MECHANISMS

Soft robotics is a widely and rapidly growing field of research today. Soft pneumatic actuators, as a fundamental element in soft robotics, have gained huge popularity and are being employed for the development of soft robots. During the last decade, a variety of hyper-elastic robotic systems have been realized. As the name suggests, such robots are made up of soft materials, and do not have any underlying rigid mechanical structure. These robots are actuated employing various methods like pneumatic, electroactive, jamming etc. Generally, in order to achieve a desired mechanical response to produce required actuation or manipulation, two or more materials having different stiffness are utilized to develop a soft robot. However, this method introduces complications in the fabrication process as well as in further design flexibility and modifications. The current work presents a design scheme of a soft robotic actuator adapting an easier fabrication approach, which is economical and environment friendly as well. The purpose is the realization of a soft pneumatic actuator having functional ability to produce effective actuation, and which is further employable to develop modular and scalable mechanisms. That infers to scrutinize the profile and orientation of the internal actuation cavity and the outer shape of viii the actuator. Utilization of a single material for this actuator has been considered to make this design scheme convenient. A commercial silicone rubber was selected which served for an economical process both in terms of the cost as well as its accommodating fabrication process through molding. In order to obtain the material behavior, \u2018Ansys Workbench 17.1 R \u2019 has been used. Cubic outline for the actuator aided towards the realization of a body shape which can easily be engaged for the development of modular mechanisms employing multiple units. This outer body shape further facilitates to achieve the stability and portability of the actuator. The soft actuator has been named \u2018Soft Cubic Module\u2019 based on its external cubic shape. For the internal actuation cavity design, various shapes, such as spherical, elliptical and cylindrical, were examined considering their different sizes and orientations within the cubic module. These internal cavities were simulated in order to achieve single degree of freedom actuation. That means, only one face of the cube is principally required to produce effective deformation. \u2018Creo Perametric 3.0 M 130\u2019 has been used to design the model and to evaluate the performance of actuation cavities in terms of effective deformation and the resulting von-mises stress. Out of the simulated profiles, cylindrical cavity with desired outcomes has been further considered to design the soft actuator. \u2018Ansys Workbench 17.1 R \u2019 environment was further used to assess the performance of cylindrical actuation cavity. Evaluation in two different simulation environments helped to validate the initially achieved results. The developed soft cubic actuator was then employed to develop different mechanisms in a single unit configuration as well as multi-unit robotic system developments. This design scheme is considered as the first tool to investigate its capacity to perform certain given tasks in various configurations. Alongside its application as a single unit gripper and a two unit bio-mimetic crawling mechanism, this soft actuator has been employed to realize a four degree ix of freedom robotic mechanism. The formation of this primitive soft robotic four axis mechanism is being further considered to develop an equivalent mechanism similar to the well known Stewart platform, with advantages of compactness, simpler kinematics design, easier control, and lesser cost. Overall, the accomplished results indicate that the design scheme of Soft Cubic Module is helpful in realizing a simple and cost-effective soft pneumatic actuator which is modular and scalable. Another favourable point of this scheme is the use of a single material with convenient fabrication and handling

Learning Feedback Terms for Reactive Planning and Control

With the advancement of robotics, machine learning, and machine perception, increasingly more robots will enter human environments to assist with daily tasks. However, dynamically-changing human environments requires reactive motion plans. Reactivity can be accomplished through replanning, e.g. model-predictive control, or through a reactive feedback policy that modifies on-going behavior in response to sensory events. In this paper, we investigate how to use machine learning to add reactivity to a previously learned nominal skilled behavior. We approach this by learning a reactive modification term for movement plans represented by nonlinear differential equations. In particular, we use dynamic movement primitives (DMPs) to represent a skill and a neural network to learn a reactive policy from human demonstrations. We use the well explored domain of obstacle avoidance for robot manipulation as a test bed. Our approach demonstrates how a neural network can be combined with physical insights to ensure robust behavior across different obstacle settings and movement durations. Evaluations on an anthropomorphic robotic system demonstrate the effectiveness of our work.Comment: 8 pages, accepted to be published at ICRA 2017 conferenc