Title: Towards snap sensing

Authors: Juan Rojas; Kensuke Harada; Hiromu Onda; Natsuki Yamanobe; Eiichi Yoshida; Kazuyuki Nagata; Yoshihiro Kawai

Addresses: Intelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, 305-8568, Japan ' Intelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, 305-8568, Japan ' Intelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, 305-8568, Japan ' Intelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, 305-8568, Japan ' Intelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, 305-8568, Japan ' Intelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, 305-8568, Japan ' Intelligent Systems Research Institute, AIST, Tsukuba, Ibaraki, 305-8568, Japan

Abstract: Automating snap assemblies is highly desirable but challenging due to their varied geometrical configurations and elastic components. A key aspect to automating snap assemblies is robot state estimation and corrective motion generation, here defined as snap sensing. While progress is being made, there are yet no robust systems that allow for snap sensing. To this end we have integrated a framework that consists of a control strategy and control framework that generalises to cantilever snaps of varying geometrical complexity. We have also integrated a robot state verification method (RCBHT) that encodes FT data to yield high-level intuitive behaviours and perform output verification. Optimisation procedures and Bayesian filtering have been included in the RCBHT to increase robustness and granularity. The system provides belief states for higher level behaviours allowing probabilistic state estimation and outcome verification. In this work, preliminary assembly failure characterisation has been conducted and provides insights into assembly failure dynamics. The results, though still in simulation, are promising as the framework has effectively executed cantilever snap assemblies and robust robot state estimation with parts of varying complexity in two different robotic systems.

Keywords: active sensing; assembly automation; snap assemblies; force control; failure detection; signal processing; probabilistic assembly; Bayesian filtering; optimisation; hierarchical taxonomy; robot state estimation; corrective motion generation; robot control; robotic assembly; failure dynamics; simulation.

DOI: 10.1504/IJMA.2013.053409

International Journal of Mechatronics and Automation, 2013 Vol.3 No.2, pp.69 - 93

Available online: 17 Apr 2013 *

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