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자료유형
학술대회자료
저자정보
Luka Petrović (University of Zagreb Faculty of Electrical Engineering and Computing) Filip Marić (University of Zagreb Faculty of Electrical Engineering and Computing) Ivan Marković (University of Zagreb Faculty of Electrical Engineering and Computing) Jonathan Kelly (University of Toronto Institute for Aerospace Studies) Ivan Petrović (University of Zagreb Faculty of Electrical Engineering and Computing)
저널정보
제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2021
발행연도
2021.10
수록면
1,760 - 1,765 (6page)

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One of the principal challenges in motion planning for robotic arms is to ensure agility in the case of encountering unforeseeable changes during task execution. It is thus crucial to preserve the ability to move in every direction in task space, which is achieved by avoiding singularities, i.e., states of configuration space where degrees of freedom are lost. To aid in singularity avoidance, existing methods mostly rely on manipulability or dexterity indices to provide a measure of proximity to singular configurations. Recently, a novel geometry-aware singularity index was proposed that circumvents some of the failure modes inherent to manipulability and dexterity. In this paper, we propose a cost function based on this index and integrate it within a stochastic trajectory optimization framework for efficient motion planning with singularity avoidance. We compare the proposed method with existing singularity-aware motion planning techniques, demonstrating improvement in common indices such as manipulability and dexterity and showcasing the ability of the proposed method to handle collision avoidance while retaining agility of the robot arm.

목차

Abstract
1. INTRODUCTION
2. BACKGROUND
3. STOCHASTIC TRAJECTORY OPTIMIZATION WITH SINGULARITY AVOIDANCE
4. EXPERIMENTAL RESULTS
5. CONCLUSION AND FUTUREWORK
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