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Contact events classes for the robotic gripper and roughneck. Labels 1 and 2 represent sensor placement locations on the robotic gripper setup detailed in Section IV-B The system manipulates pipes with a robotic gripper and a roughneck (Fig. 1). Both the gripper and roughneck grasp steel pipes with hardened steel dies. Contact events (including impacts, slips, and other unspecified sources of noise) may occur at either site.
Source publication
To ensure safe and reliable operation in a robotic oil drilling system, it is essential to detect contact events such as impacts and slips between end-effectors and workpieces. In this challenging application, where high forces are used to manipulate heavy metal pipes in noisy environments, acoustic emissions (AE) sensors offer a promising contact...
Contexts in source publication
Context 1
... robotic gripper's primary function is to transfer pipes and move them into or out of the roughneck. During pipe transfer and placement, slips may occur along the pipe's axis. However, rotational slips about the pipe's long axis are unlikely. Contact event classes for the gripper are: (I) linear slip, (V) impact, and (VI) noise (Fig. ...
Context 2
... of steel dies. Experiments were performed using a 253 mm pipe. The same AE sensors were attached to various locations on the gripper, with couplant grease at the interface (Fig. 6). Grip force was held constant throughout each trial via closed-loop control. 2) Procedure: The same gripper contact events (I) linear slip, (V) impact, and (VI) noise (Fig. 2) were tested. Linear slip and impact trial protocols were unchanged with respect to the benchtop apparatus. Noise was introduced via hydraulic servo valves mounted on the system's frame. Two locations were tested (Fig. 6): location 1 is analogous to the benchtop setup in terms of AE coupling, location 2 is RDS's desired sensor placement ...
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For small legged robots, ground contact interactions significantly affect the dynamics and locomotion performance. In this article, we designed thin, robust capacitive tactile sensors and applied them to the feet of a small hexapod with C-shaped rotating legs. The sensors measure contact forces as the robot traverses different types of terrain including hard surfaces with high or low friction, sand, and grass. Different gaits perform best on different types of terrain. Useful measured parameters include the magnitude and timing of the peak normal forces, in combination with the leg rotational velocity. The measured parameters were used in a support vector machine classifier to identify terrain types with 82.5% accuracy. Based on gait performance studies, we implemented a terrain-based gait control using real-time terrain classifications. A surface transitioning test shows 17.1% increase in body speed and 13.2% improvement in efficiency as the robot adjusts its gait.
... To the best of our knowledge, there is no investigation on grasp planning of a grasped object failure. Even the works which consider avoiding deflection and/or slippage of the object [5], [6], [7] do not study the individual effects of bending, tension, or torsion on the object which is essential for obtaining an accurate characterization of object failure grasp task. 1 Mahyar Abdeetedal and Mehrdad R. Kermani Grasp tasks can be characterized by a set of expected wrenches that the grasp must withstand while being manipulated [8]. A task polytope can be defined using all these wrenches [9] which are also called Task Wrench Space (TWS). ...
This paper investigates the less-studied problem of failing/yielding an object purposefully by a robotic hand. A grasp synthesis capable of using the whole limb surface of the robotic hand is designed based on internal force decomposition. The introduced approach is based on quasistatic assumption and optimization of active internal forces in order to counterbalance the formulated task wrench/load of yielding. As different geometrical constraints are dictated by the manipulation circumstances (e.g. metallic sheet shaping or robotic harvesting), the yielding wrench optimization is developed to be not only sufficient for yielding the object but also effective in meeting all motion restrictions on manipulator. Maximum-shear-stress theory is used for yielding analysis of a grasped object. Finite Element Modeling (FEM) simulation results are provided as a validation of our proposed approach.
This chapter provides an overview of force and tactile sensing, with the primary emphasis placed on tactile sensing. We begin by presenting some basic considerations in choosing a tactile sensor and then review a wide variety of sensor types, including proximity, kinematic, force, dynamic, contact, skin deflection, thermal, and pressure sensors. We also review various transduction methods, appropriate for each general sensor type. We consider the information that these various types of sensors provide in terms of whether they are most useful for manipulation, surface exploration or being responsive to contacts from external agents.
Concerning the interpretation of tactile information, we describe the general problems and present two short illustrative examples. The first involves intrinsic tactile sensing, i. e., estimating contact locations and forces from force sensors. The second involves contact pressure sensing, i. e., estimating surface normal and shear stress distributions from an array of sensors in an elastic skin. We conclude with a brief discussion of the challenges that remain to be solved in packaging and manufacturing damage-tolerant tactile sensors.
