Grasping bulky objects with two anthropomorphic hands

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We then process the point clouds dividing them into two set of slices where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, the triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the corresponding points are reachable by the hands, if so, motion planning and a collision check are performed to asses if the final grasp configuration of the system is suitable. The paper inclu des some application examples of the proposed approachAccepted versio

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

Towards Developing Gripper to obtain Dexterous Manipulation

Artificial hands or grippers are essential elements in many robotic systems, such as, humanoid, industry, social robot, space robot, mobile robot, surgery and so on. As humans, we use our hands in different ways and can perform various maneuvers such as writing, altering posture of an object in-hand without having difficulties. Most of our daily activities are dependent on the prehensile and non-prehensile capabilities of our hand. Therefore, the human hand is the central motivation of grasping and manipulation, and has been explicitly studied from many perspectives such as, from the design of complex actuation, synergy, use of soft material, sensors, etc; however to obtain the adaptability to a plurality of objects along with the capabilities of in-hand manipulation of our hand in a grasping device is not easy, and not fully evaluated by any developed gripper. Industrial researchers primarily use rigid materials and heavy actuators in the design for repeatability, reliability to meet dexterity, precision, time requirements where the required flexibility to manipulate object in-hand is typically absent. On the other hand, anthropomorphic hands are generally developed by soft materials. However they are not deployed for manipulation mainly due to the presence of numerous sensors and consequent control complexity of under-actuated mechanisms that significantly reduce speed and time requirements of industrial demand. Hence, developing artificial hands or grippers with prehensile capabilities and dexterity similar to human like hands is challenging, and it urges combined contributions from multiple disciplines such as, kinematics, dynamics, control, machine learning and so on. Therefore, capabilities of artificial hands in general have been constrained to some specific tasks according to their target applications, such as grasping (in biomimetic hands) or speed/precision in a pick and place (in industrial grippers). Robotic grippers developed during last decades are mostly aimed to solve grasping complexities of several objects as their primary objective. However, due to the increasing demands of industries, many issues are rising and remain unsolved such as in-hand manipulation and placing object with appropriate posture. Operations like twisting, altering orientation of object within-hand, require significant dexterity of the gripper that must be achieved from a compact mechanical design at the first place. Along with manipulation, speed is also required in many robotic applications. Therefore, for the available speed and design simplicity, nonprehensile or dynamic manipulation is widely exploited. The nonprehensile approach however, does not focus on stable grasping in general. Also, nonprehensile or dynamic manipulation often exceeds robot\u2019s kinematic workspace, which additionally urges installation of high speed feedback and robust control. Hence, these approaches are inapplicable especially when, the requirements are grasp oriented such as, precise posture change of a payload in-hand, placing payload afterward according to a strict final configuration. Also, addressing critical payload such as egg, contacts (between gripper and egg) cannot be broken completely during manipulation. Moreover, theoretical analysis, such as contact kinematics, grasp stability cannot predict the nonholonomic behaviors, and therefore, uncertainties are always present to restrict a maneuver, even though the gripper is capable of doing the task. From a technical point of view, in-hand manipulation or within-hand dexterity of a gripper significantly isolates grasping and manipulation skills from the dependencies on contact type, a priory knowledge of object model, configurations such as initial or final postures and also additional environmental constraints like disturbance, that may causes breaking of contacts between object and finger. Hence, the property (in-hand manipulation) is important for a gripper in order to obtain human hand skill. In this research, these problems (to obtain speed, flexibility to a plurality of grasps, within-hand dexterity in a single gripper) have been tackled in a novel way. A gripper platform named Dexclar (DEXterous reConfigurable moduLAR) has been developed in order to study in-hand manipulation, and a generic spherical payload has been considered at the first place. Dexclar is mechanism-centric and it exploits modularity and reconfigurability to the aim of achieving within-hand dexterity rather than utilizing soft materials. And hence, precision, speed are also achievable from the platform. The platform can perform several grasps (pinching, form closure, force closure) and address a very important issue of releasing payload with final posture/ configuration after manipulation. By exploiting 16 degrees of freedom (DoF), Dexclar is capable to provide 6 DoF motions to a generic spherical or ellipsoidal payload. And since a mechanism is reliable, repeatable once it has been properly synthesized, precision and speed are also obtainable from them. Hence Dexclar is an ideal starting point to study within-hand dexterity from kinematic point of view. As the final aim is to develop specific grippers (having the above capabilities) by exploiting Dexclar, a highly dexterous but simply constructed reconfigurable platform named VARO-fi (VARiable Orientable fingers with translation) is proposed, which can be used as an industrial end-effector, as well as an alternative of bio-inspired gripper in many robotic applications. The robust four fingered VARO-fi addresses grasp, in-hand manipulation and release (payload with desired configuration) of plurality of payloads, as demonstrated in this thesis. Last but not the least, several tools and end-effectors have been constructed to study prehensile and non-prehensile manipulation, thanks to Bayer Robotic challenge 2017, where the feasibility and their potentiality to use them in an industrial environment have been validated. The above mentioned research will enhance a new dimension for designing grippers with the properties of dexterity and flexibility at the same time, without explicit theoretical analysis, algorithms, as those are difficult to implement and sometime not feasible for real system

Robocatch: Design and Making of a Hand-Held Spillage-Free Specimen Retrieval Robot for Laparoscopic Surgery

Specimen retrieval is an important step in laparoscopy, a minimally invasive surgical procedure performed to diagnose and treat a myriad of medical pathologies in fields ranging from gynecology to oncology. Specimen retrieval bags (SRBs) are used to facilitate this task, while minimizing contamination of neighboring tissues and port-sites in the abdominal cavity. This manual surgical procedure requires usage of multiple ports, creating a traffic of simultaneous operations of multiple instruments in a limited shared workspace. The skill-demanding nature of this procedure makes it time-consuming, leading to surgeons’ fatigue and operational inefficiency. This thesis presents the design and making of RoboCatch, a novel hand-held robot that aids a surgeon in performing spillage-free retrieval of operative specimens in laparoscopic surgery. The proposed design significantly modifies and extends conventional instruments that are currently used by surgeons for the retrieval task: The core instrumentation of RoboCatch comprises a webbed three-fingered grasper and atraumatic forceps that are concentrically situated in a folded configuration inside a trocar. The specimen retrieval task is achieved in six stages: 1) The trocar is introduced into the surgical site through an instrument port, 2) the three webbed fingers slide out of the tube and simultaneously unfold in an umbrella like-fashion, 3) the forceps slide toward, and grasp, the excised specimen, 4) the forceps retract the grasped specimen into the center of the surrounding grasper, 5) the grasper closes to achieve a secured containment of the specimen, and 6) the grasper, along with the contained specimen, is manually removed from the abdominal cavity. The resulting reduction in the number of active ports reduces obstruction of the port-site and increases the procedure’s efficiency. The design process was initiated by acquiring crucial parameters from surgeons and creating a design table, which informed the CAD modeling of the robot structure and selection of actuation units and fabrication material. The robot prototype was first examined in CAD simulation and then fabricated using an Objet30 Prime 3D printer. Physical validation experiments were conducted to verify the functionality of different mechanisms of the robot. Further, specimen retrieval experiments were conducted with porcine meat samples to test the feasibility of the proposed design. Experimental results revealed that the robot was capable of retrieving masses of specimen ranging from 1 gram to 50 grams. The making of RoboCatch represents a significant step toward advancing the frontiers of hand-held robots for performing specimen retrieval tasks in minimally invasive surgery

Haptic Exploration of Unknown Objects for Robust in-hand Manipulation.

Human-like robot hands provide the flexibility to manipulate a variety of objects that are found in unstructured environments. Knowledge of object properties and motion trajectory is required, but often not available in real-world manipulation tasks. Although it is possible to grasp and manipulate unknown objects, an uninformed grasp leads to inferior stability, accuracy, and repeatability of the manipulation. Therefore, a central challenge of in-hand manipulation in unstructured environments is to acquire this information safely and efficiently. We propose an in-hand manipulation framework that does not assume any prior information about the object and the motion, but instead extracts the object properties through a novel haptic exploration procedure and learns the motion from demonstration using dynamical movement primitives. We evaluate our approach by unknown object manipulation experiments using a human-like robot hand. The results show that haptic exploration improves the manipulation robustness and accuracy significantly, compared to the virtual spring framework baseline method that is widely used for grasping unknown objects