Multifingered grasping for robotic manipulation

Robotic hand increases the adaptability of grasping and manipulating objects with its system.But this added adaptability of grasping convolute the process of grasping the object. The analysis of the grasp is very much complicated and large number of configuration for grasping is to be investigated. Handling of objects with irregular shapes and that of flexible/soft objects by ordinary robot grippers is difficult. It is required that various objects with different shapes or sizes could be grasped and manipulated by one robot hand mechanism for the sake of factory automation and labour saving. Dexterous grippers will be the appropriate solution to such problems. Corresponding to such needs, the present work is towards the design and development of an articulated mechanical hand with five fingers and twenty five degrees-of-freedom having an improved grasp capability. In the work, the distance between the Thumb and Finger and the workspace generated by the hand is calculated so as to know about the size and shape of the object that could be grasped.Further the Force applied by the Fingers and there point of application is also being calculated so as to have a stable force closure grasp. The method introduced in present study reduces the complexity and computational burden of grasp synthesis by examining grasps at the finger level. A detailed study on the force closure grasping capability and quality has been carried out. The workspace of the five fingered hand has been used as the maximum spatial envelope. The problem has been considered with positive grips constructed as non-negative linear combinations of primitive and pure wrenches. The attention has been restricted to systems of wrenches generated by the hand fingers assuming Coulomb friction. In order to validate the algorithm vis-a-vis the designed five fingered dexterous hand, example problems have been solved with multiple sets of contact points on various shaped objects.Since the designed hand is capable of enveloping and grasping an object mechanically, it can be used conveniently and widely in manufacturing automation and for medical rehabilitation purpose. This work presents the kinematic design and the grasping analysis of such a hand

Parallelizing RRT on large-scale distributed-memory architectures

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree (RRT) algorithm on large-scale distributed-memory architectures, using the Message Passing Interface. We compare three parallel versions of RRT based on classical parallelization schemes. We evaluate them on different motion planning problems and analyze the various factors influencing their performance

Progettazione e Controllo di Mani Robotiche

The application of dexterous robotic hands out of research laboratories has been limited by the intrinsic complexity that these devices present. This is directly reflected as an economically unreasonable cost and a low overall reliability. Within the research reported in this thesis it is shown how the problem of complexity in the design of robotic hands can be tackled, taking advantage of modern technologies (i.e. rapid prototyping), leading to innovative concepts for the design of the mechanical structure, the actuation and sensory systems. The solutions adopted drastically reduce the prototyping and production costs and increase the reliability, reducing the number of parts required and averaging their single reliability factors. In order to get guidelines for the design process, the problem of robotic grasp and manipulation by a dual arm/hand system has been reviewed. In this way, the requirements that should be fulfilled at hardware level to guarantee successful execution of the task has been highlighted. The contribution of this research from the manipulation planning side focuses on the redundancy resolution that arise in the execution of the task in a dexterous arm/hand system. In literature the problem of coordination of arm and hand during manipulation of an object has been widely analyzed in theory but often experimentally demonstrated in simplified robotic setup. Our aim is to cover the lack in the study of this topic and experimentally evaluate it in a complex system as a anthropomorphic arm hand system

An Asynchronous Simulation Framework for Multi-User Interactive Collaboration: Application to Robot-Assisted Surgery

The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator

Kinematic Analysis of Multi-Fingered, Anthropomorphic Robotic Hands

The ability of stable grasping and fine manipulation with the multi-fingered robot hand with required precision and dexterity is playing an increasingly important role in the applications like service robots, rehabilitation, humanoid robots, entertainment robots, industries etc.. A number of multi-fingered robotic hands have been developed by various researchers in the past. The distinct advantages of a multi-fingered robot hand having structural similarity with human hand motivate the need for an anthropomorphic robot hand. Such a hand provides a promising base for supplanting human hand in execution of tedious, complicated and dangerous tasks, especially in situations such as manufacturing, space, undersea etc. These can also be used in orthopaedic rehabilitation of humans for improving the quality of the life of people having orthopedically and neurological disabilities. The developments so far are mostly driven by the application requirements. There are a number of bottlenecks with industrial grippers as regards to the stability of grasping objects of irregular geometries or complex manipulation operations. A multi-fingered robot hand can be made to mimic the movements of a human hand. The present piece of research work attempts to conceptualize and design a multi-fingered, anthropomorphic robot hand by structurally imitating the human hand. In the beginning, a brief idea about the history, types of robotic hands and application of multi-fingered hands in various fields are presented. A review of literature based on different aspects of the multi-fingered hand like structure, control, optimization, gasping etc. is made. Some of the important and more relevant literatures are elaborately discussed and a brief analysis is made on the outcomes and shortfalls with respect to multi-fingered hands. Based on the analysis of the review of literature, the research work aims at developing an improved anthropomorphic robot hand model in which apart from the four fingers and a thumb, the palm arch effect of human hand is also considered to increase its dexterity. A robotic hand with five anthropomorphic fingers including the thumb and palm arch effect having 25 degrees-of-freedom in all is investigated in the present work. Each individual finger is considered as an open loop kinematic chain and each finger segment is considered as a link of the manipulator. The wrist of the hand is considered as a fixed point. The kinematic analyses of the model for both forward kinematics and inverse kinematic are carried out. The trajectories of the tip positions of the thumb and the fingers with respect to local coordinate system are determined and plotted. This gives the extreme position of the fingertips which is obtained from the forward kinematic solution with the help of MATLAB. Similarly, varying all the joint iv angles of the thumb and fingers in their respective ranges, the reachable workspace of the hand model is obtained. Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for solving the inverse kinematic problem of the fingers. Since the multi-fingered hand grasps the object mainly through its fingertips and the manipulation of the object is facilitated by the fingers due to their dexterity, the grasp is considered to be force-closure grasp. The grasping theory and different types of contacts between the fingertip and object are presented and the conditions for stable and equilibrium grasp are elaborately discussed. The proposed hand model is simulated to grasp five different shaped objects with equal base dimension and height. The forces applied on the fingertip during grasping are calculated. The hand model is also analysed using ANSYS to evaluate the stresses being developed at various points in the thumb and fingers. This analysis was made for the hand considering two different hand materials i.e. aluminium alloy and structural steel. The solution obtained from the forward kinematic analysis of the hand determines the maximum size for differently shaped objects while the solution to the inverse kinematic problem indicates the configurations of the thumb and the fingers inside the workspace of the hand. The solutions are predicted in which all joint angles are within their respective ranges. The results of the stress analysis of the hand model show that the structure of the fingers and the hand as a whole is capable of handling the selected objects. The robot hand under investigation can be realized and can be a very useful tool for many critical areas such as fine manipulation of objects, combating orthopaedic or neurological impediments, service robotics, entertainment robotics etc. The dissertation concludes with a summary of the contribution and the scope of further work

Muscle activation mapping of skeletal hand motion: an evolutionary approach.

Creating controlled dynamic character animation consists of mathe- matical modelling of muscles and solving the activation dynamics that form the key to coordination. But biomechanical simulation and control is com- putationally expensive involving complex di erential equations and is not suitable for real-time platforms like games. Performing such computations at every time-step reduces frame rate. Modern games use generic soft- ware packages called physics engines to perform a wide variety of in-game physical e ects. The physics engines are optimized for gaming platforms. Therefore, a physics engine compatible model of anatomical muscles and an alternative control architecture is essential to create biomechanical charac- ters in games. This thesis presents a system that generates muscle activations from captured motion by borrowing principles from biomechanics and neural con- trol. A generic physics engine compliant muscle model primitive is also de- veloped. The muscle model primitive forms the motion actuator and is an integral part of the physical model used in the simulation. This thesis investigates a stochastic solution to create a controller that mimics the neural control system employed in the human body. The control system uses evolutionary neural networks that evolve its weights using genetic algorithms. Examples and guidance often act as templates in muscle training during all stages of human life. Similarly, the neural con- troller attempts to learn muscle coordination through input motion samples. The thesis also explores the objective functions developed that aids in the genetic evolution of the neural network. Character interaction with the game world is still a pre-animated behaviour in most current games. Physically-based procedural hand ani- mation is a step towards autonomous interaction of game characters with the game world. The neural controller and the muscle primitive developed are used to animate a dynamic model of a human hand within a real-time physics engine environment

Integrating Optimization and Sampling for Robot Motion Planning with Applications in Healthcare

Robots deployed in human-centric environments, such as a person's home in a home-assistance setting or inside a person's body in a surgical setting, have the potential to have a large, positive impact on human quality of life. However, for robots to operate in such environments they must be able to move efficiently while avoiding colliding with obstacles such as objects in the person's home or sensitive anatomical structures in the person's body. Robot motion planning aims to compute safe and efficient motions for robots that avoid obstacles, but home assistance and surgical robots come with unique challenges that can make this difficult. For instance, many state of the art surgical robots have computationally expensive kinematic models, i.e., it can be computationally expensive to predict their shape as they move. Some of these robots have hybrid dynamics, i.e., they consist of multiple stages that behave differently. Additionally, it can be difficult to plan motions for robots while leveraging real-world sensor data, such as point clouds. In this dissertation, we demonstrate and empirically evaluate methods for overcoming these challenges to compute high-quality and safe motions for robots in home-assistance and surgical settings. First, we present a motion planning method for a continuum, parallel surgical manipulator that accounts for its computationally expensive kinematics. We then leverage this motion planner to optimize its kinematic design chosen prior to a surgical procedure. Next, we present a motion planning method for a 3-stage lung tumor biopsy robot that accounts for its hybrid dynamics and evaluate the robot and planner in simulation and in inflated porcine lung tissue. Next, we present a motion planning method for a home-assistance robot that leverages real-world, point-cloud obstacle representations. We then expand this method to work with a type of continuum surgical manipulator, a concentric tube robot, with point-cloud anatomical representations. Finally, we present a data-driven machine learning method for more accurately estimating the shape of concentric tube robots. By effectively addressing challenges associated with home assistance and surgical robots operating in human-centric environments, we take steps toward enabling robots to have a positive impact on human quality of life.Doctor of Philosoph