Abstract
Humanoid arm has a wide range of applications such as automatic assembly and welding. Due to its complex and nonlinear properties, it is difficult to achieve high robustness and fast response synchronously for the motion planning of humanoid arm. Very recently, it has been proved that imitating human movement system can improve the performance of robot control [11]. This paper proposes a new 3D motion planning method of humanoid arm based on habitual planning theory. The method we proposed is a pre-training algorithm to map the target inputs into a series of patterns of the 3D motion space. Therefore, our proposed method can realize 3D motion planning of humanoid arm. The simulation experimental results demonstrate that our proposed method can use a finite number of patterns (143 patterns used in our experiment) to cover most areas (more than 99%) of the 3D motion space of humanoid arm.
This work was supported in part by the National Natural Science Foundation of China (61422307 and 61673361) and the Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars. Authors also gratefully acknowledge supports from the Youth Innovation Promotion Association, Chinese Academy of Sciences, the Youth Top-notch Talent Support Program and the Youth Yangtze River Scholar.
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Chen, S., Cui, Y., Kang, Y., Cao, Y., Song, W. (2017). 3D Motions Planning of Humanoid Arm Using Learned Patterns. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_36
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