Enhancing in-hand dexterous micro-manipulation for real-time applications

Abstract

This paper presents a new approach of planar trajectory generation for automated in-hand dexterous manipulation of miniaturized objects. The proposed method aims at improving the efficiency of the previous method [17] to be able to perform real-time in-hand manipulation trajectories generation. The main idea behind this new method is the representation of the configuration space as a set of stable rotations instead of stable grasps as it is usually done. The consequence of this representation is a more compact space that encapsulates more information. The developed algorithm that uses this approach is able to generate optimal trajectories to manipulate complex objects in less than 0.1 s, which represents a reduction in the processing time between 10 2 and 10 5 compared to the previous method.

Cyber-Physical Systems for Micro-/Nano-assembly Operations: a Survey

“…Later researches introduced vision recognition of micro/ nano-components and learning algorithms applied to recognition and tracking of micro-objects [35] presents a complete review on learning-based approaches to perform general gripping operations at the macro-scale. To enhance dexterity and optimize the grasping trajectories, authors in [36] [37•] exploited physical models of grasping forces and pull-off forces during micro-manipulation operations using search algorithms for a real-time application which computes the gripping trajectory in less than 0.1 s. van Vuuren JJ et al [38] propose a learning-based methodology for identifying novel objects and evaluating different candidate grasping strategies for an optimal grasping and handling which might be applied to the manufacturing of consumer electronic products. Learning methods may be used as well to perform the manipulation of flexible objects based on deformation models of elastic behaviours [39], enabling the system in real-time.…”