Evaluation and selection of grasp quality criteria for dexterous manipulation

Mnyussiwalla, H.; Seguin, Pascal; Vulliez, P.; Gazeau, Jean Pierre

doi:10.1007/s10846-021-01554-4

Cited by 9 publications

(1 citation statement)

References 27 publications

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“…The waste collection task makes it necessary to address not only its detection and location but also the grasping of objects [13], as already implemented in the robotic field. For instance, [7] proposed a mathematical method with which to calculate the best pair of grasping points from a 3D-scene point cloud.…”

Section: Visual Perceptionmentioning

confidence: 99%

Vision and Tactile Robotic System to Grasp Litter in Outdoor Environments

Páez-Ubieta,

Castaño-Amorós,

Puente

et al. 2023

J Intell Robot Syst

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The accumulation of litter is increasing in many places and is consequently becoming a problem that must be dealt with. In this paper, we present a manipulator robotic system to collect litter in outdoor environments. This system has three functionalities. Firstly, it uses colour images to detect and recognise litter comprising different materials. Secondly, depth data are combined with pixels of waste objects to compute a 3D location and segment three-dimensional point clouds of the litter items in the scene. The grasp in 3 Degrees of Freedom (DoFs) is then estimated for a robot arm with a gripper for the segmented cloud of each instance of waste. Finally, two tactile-based algorithms are implemented and then employed in order to provide the gripper with a sense of touch. This work uses two low-cost visual-based tactile sensors at the fingertips. One of them addresses the detection of contact (which is obtained from tactile images) between the gripper and solid waste, while another has been designed to detect slippage in order to prevent the objects grasped from falling. Our proposal was successfully tested by carrying out extensive experimentation with different objects varying in size, texture, geometry and materials in different outdoor environments (a tiled pavement, a surface of stone/soil, and grass). Our system achieved an average score of 94% for the detection and Collection Success Rate (CSR) as regards its overall performance, and of 80% for the collection of items of litter at the first attempt.

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Section: Visual Perceptionmentioning

confidence: 99%