This paper proposes a novel robotic hand design for assembly tasks. The idea is to combine two simple grippers -- an inner gripper which is used for precise alignment, and an outer gripper which is used for stable holding. Conventional robotic hands require complicated compliant mechanisms or complicated control strategy and force sensing to conduct assemble tasks, which makes them costly and difficult to pick and arrange small objects like screws or washers. Compared to the conventional hands, the proposed design provides a low-cost solution for aligning, picking up, and arranging various objects by taking advantages of the geometric constraints of the positioning fingers and gravity. It is able to deal with small screws and washers, and eliminate the position errors of cylindrical objects or objects with cylindrical holes. In the experiments, both real-world tasks and quantitative analysis are performed to validate the aligning, picking, and arrangements abilities of the design.
There are also designs that use sensors and feedback control to implement the desired functions. For example, @cite_19 designed a parallel gripper with embedded vision and force feedback to adjust poses and perform insertion tasks. @cite_2 developed a swivel mechanism to sense and position the surface of target objects. It is also an example of positioning using sensors.
@cite_19 @cite_2
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In-hand manipulation is certainly one of the most challenging problems in robotic manipulation. Solutions to this problem depend on the specific device used to grab the object, but nowadays, the trend is to exploit not only the gripper but also external constraints, such as other objects in the environment or external forces, like gravity. This allows a robot to manipulate an object even with very simple grippers, like a parallel gripper. Nevertheless, even for a simple grasping task, which aims at grabbing the object with a given fixed orientation or for executing a controlled slip, information on the contact between the fingers of the gripper and the object is relevant. In these cases, both linear and rotational slipping should be controlled during the grasping phase and during the motion phase. The present paper proposes a control strategy for the first objective, namely slipping avoidance. The strategy is based on contact information provided by a six-axis force tactile sensor, able to measure contact force and torque as well as able to provide information on the contact geometry, that means orientation of the object with respect to the gripper. Experiments on a parallel gripper sensorized with a new force tactile sensor and mounted on a Kuka iiwa show how the strategy successfully allows the robot to safely manipulate a rigid object in various friction conditions of its surface. We present a novel robotic grasp controller that allows a sensorized parallel jaw gripper to gently pick up and set down unknown objects once a grasp location has been selected. Our approach is inspired by the control scheme that humans employ for such actions, which is known to centrally depend on tactile sensation rather than vision or proprioception. Our controller processes measurements from the gripper's fingertip pressure arrays and hand-mounted accelerometer in real time to generate robotic tactile signals that are designed to mimic human SA-I, FA-I, and FA-II channels. These signals are combined into tactile event cues that drive the transitions between six discrete states in the grasp controller: Close, Load, Lift and Hold, Replace, Unload, and Open. The controller selects an appropriate initial grasping force, detects when an object is slipping from the grasp, increases the grasp force as needed, and judges when to release an object to set it down. We demonstrate the promise of our approach through implementation on the PR2 robotic platform, including grasp testing on a large number of real-world objects. We present a complete software architecture for reliable grasping of household objects. Our work combines aspects such as scene interpretation from 3D range data, grasp planning, motion planning, and grasp failure identification and recovery using tactile sensors. We build upon, and add several new contributions to the significant prior work in these areas. A salient feature of our work is the tight coupling between perception (both visual and tactile) and manipulation, aiming to address the uncertainty due to sensor and execution errors. This integration effort has revealed new challenges, some of which can be addressed through system and software engineering, and some of which present opportunities for future research. Our approach is aimed at typical indoor environments, and is validated by long running experiments where the PR2 robotic platform was able to consistently grasp a large variety of known and unknown objects. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS). In this work we present a visual servoing approach that enables a humanoid robot to robustly execute dual arm grasping and manipulation tasks. Therefore the target object(s) and both hands are tracked alternately and a combined open- closed-loop controller is used for positioning the hands with respect to the target(s). We address the perception system and how the observable workspace can be increased by using an active vision system on a humanoid head. Furthermore a control framework for reactive positioning of both hands using position based visual servoing is presented, where the sensor data streams coming from the vision system, the joint encoders and the force torque sensors are fused and joint velocity values are generated. This framework can be used for bimanual grasping as well as for two handed manipulations which is demonstrated with the humanoid robot Armar-III that executes grasping and manipulation tasks in a kitchen environment.