A Three Finger Tendon Driven Robotic Hand Design and Its Kinematics Model

Sainul, IA; Deb, Sankha; Deb, Alok Kanti

doi:10.1007/978-81-322-2740-3_30

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The other two fingers have three joints each, and the base joints help the fingers to spread sideways synchronously around an axis perpendicular to the palm surface. The underactuation mechanism [28] works in such a way that a link in an articulated finger only moves after its previous link touches an object or maximum joint limit is reached. Finally, the third one is a simple two-finger adaptive gripper having two links in each finger as shown in Fig.…”

Section: Robotic Hands/grippersmentioning

confidence: 99%

A novel object slicing-based grasp planner for unknown 3D objects

Ansary

Deb

2021

Intel Serv Robotics

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Robotic grasp planning has been one of the active areas of research in robotics but still remains a challenging problem for arbitrary objects even in completely known environments. Most previously developed algorithms had focused on precision/fingertip type of grasps, failing to solve the problem even for fully actuated hands/grippers during enveloping/adaptive/wrapping/power type of grasps, where each finger makes contact with an object at several points. Kinematic closed-form solutions are not possible for such an articulated finger, which simultaneously reaches several given goal points. This paper presents a framework for computing the best grasp for robotic hands/grippers, based on a novel object slicing method. The proposed method quickly finds contacts using an object slicing technique and uses a grasp quality measure to find the best grasp from a pool of pre-grasps. The pool of pre-grasps is generated by dividing the objects into parts and organizing them in a decomposition tree structure, where the parts are approximated by simple box primitives. To validate the proposed method, the developed grasp planner has been implemented on an industrial Motoman robot and a two-finger gripper. Further, the results have been compared with the state-of-the-art in grasp planning to evaluate the performance of the proposed grasp planner. As compared to other existing approaches, the proposed approach has several advantages to offer. It can handle objects with complex shapes and sizes. Most importantly, it works on both point clouds taken using a depth sensor and polygonal mesh models. It takes into account hand constraints and generates feasible grasps for both adaptive/enveloping and fingertip type of grasps. KeywordsGrasp planner • Spatial search trees • Grasp quality metric • Underactuated robotic hand/gripper B Sainul Islam Ansary

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Section: Robotic Hands/grippersmentioning

confidence: 99%

A novel object slicing-based grasp planner for unknown 3D objects

Ansary

Deb

2021

Intel Serv Robotics

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“…In this section, an overview of a two finger underactuated robotic gripper and its actuation mechanism are given. The actuation mechanism of the fingers is discussed in Sainul et al [15]. The gripper consists of two identical fingers, each having three links namely knuckle, proximal, and distal links as shown in figure 1.…”

Section: Underactuated Robot Grippermentioning

confidence: 99%

A novel object slicing based grasp planner for 3D object grasping using underactuated robot gripper

Sainul

Deb

2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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Robotic grasping of arbitrary objects even in completely known environments still remains a challenging problem. Most previously developed algorithms had focused on fingertip grasp, failing to solve the problem even for fully actuated hands/grippers during adaptive/wrapping type of grasps, where each finger makes contact with object at several points. Kinematic closed form solutions are not possible for such an articulated finger which simultaneously reaches several given goal points. This paper, presents a framework for computing best grasp for an underactuated robotic gripper, based on a novel object slicing method. The proposed method quickly find contacts using an object slicing technique and use grasp quality measure to find the best grasp from a pool of grasps. To validate the proposed method, implementation has been done on twenty-four household objects and toys using a two finger underactuated robot gripper. Unlike the many other existing approaches, the proposed approach has several advantages: it can handle objects with complex shapes and sizes; it does not require simplifying the objects into primitive geometric shape; Most importantly, it can be applied on point clouds taken using depth sensor; it takes into account gripper kinematic constraints and generates feasible grasps for both adaptive/enveloping and fingertip types of grasps.

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