An overview of 3D object grasp synthesis algorithms

Sahbani, Anis; El-Khoury, Sahar; Bidaud, Philippe

doi:10.1016/j.robot.2011.07.016

Cited by 405 publications

(226 citation statements)

References 58 publications

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“…Attempts in this direction include the development of robotic hands as similar as possible to the human hand from both the aesthetic as well as the functional point of view [1] and, consequently, new approaches for reproducing human hand motion. They can resort to observation and analysis of hand motion [6], [7] or else, to learning-by-demonstration approaches [8].…”

Section: Introductionmentioning

confidence: 99%

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Cordella

Zollo

Salerno

et al. 2014

International Journal of Advanced Robotic Systems

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Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.

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Section: Introductionmentioning

confidence: 99%

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Cordella

Zollo

Salerno

et al. 2014

International Journal of Advanced Robotic Systems

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“…With moderate changes to how the hand approaches an object with complex geometry, like the champagne glass in our experiment, the set of stable hand configurations sometimes can change significantly. In order to increase the rate of grasping in scenarios that substantially differ from the setup in the demonstrations, we would need to use one of the robotic grasp synthesis algorithms to generate the final hand configuration (Sahbani et al 2012).…”

Section: Model Validation For Robot Controlmentioning

confidence: 99%

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Lukic

Santos-Victor²,

Billard

2014

Biol Cybern

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We investigate the role of obstacle avoidance in visually guided reaching and grasping movements. We report on a human study in which subjects performed prehensile motion with obstacle avoidance where the position of the obstacle was systematically varied across trials. These experiments suggest that reaching with obstacle avoidance is organized in a sequential manner, where the obstacle acts as an intermediary target. Furthermore, we demonstrate that the notion of workspace travelled by the hand is embedded explicitly in a forward planning scheme, which is actively involved in detecting obstacles on the way when performing reaching. We find that the gaze proactively coordinates the pattern of eye-arm motion during obstacle avoidance. This study provides also a quantitative assessment of the coupling between the eye-arm-hand motion. We show that the coupling follows regular phase dependencies and is unaltered during obstacle avoidance. These observations provide a basis for the design of a computational model. Our controller extends the coupled dynamical systems framework and provides fast and synchronous control of the eyes, the arm and the hand within a single and compact framework, mimicking similar control system found in humans. We validate our model for visuomotor control of a humanoid robot.

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“…Grasping is a key building block of autonomous robots and as a result it has received much attention in the last three decades [1], [2], [3], [4]. Different approaches have been studied, e.g., analytic [2] and data-driven [4].…”

Section: Introductionmentioning

confidence: 99%