A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space

doi:10.1109/TNNLS.2021.3111051

	A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space
	Xing, Dengpeng1,2 ; Li, Jiale1,2 ; Zhang, Tielin1,2 ; Xu, Bo1,2
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021-09-13
页码	12
通讯作者	Zhang, Tielin(tielin.zhang@ia.ac.cn)
摘要	In a small operational space, e.g., mesoscale or microscale, we need to control movements carefully because of fragile objects. This article proposes a novel structure based on spiking neural networks to imitate the joint function of multiple brain regions in visual guiding in the small operational space and offers two channels to achieve collision-free movements. For the state sensation, we simulate the primary visual cortex to directly extract features from multiple input images and the high-level visual cortex to obtain the object distance, which is indirectly measurable, in the Cartesian coordinates. Our approach emulates the prefrontal cortex from two aspects: multiple liquid state machines to predict distances of the next several steps based on the preceding trajectory and a block-based excitation-inhibition feedforward network to plan movements considering the target and prediction. Responding to ``too close'' states needs rich temporal information, and we leverage a cerebellar network for the subconscious reaction. From the viewpoint of the inner pathway, they also form two channels. One channel starts from state extraction to attraction movement planning, both in the camera coordinates, behaving visual-servo control. The other is the collision-avoidance channel, which calculates distances, predicts trajectories, and reacts to the repulsion, all in the Cartesian coordinates. We provide appropriate supervised signals for coarse training and apply reinforcement learning to modify synapses in accordance with reality. Simulation and experiment results validate the proposed method.
关键词	Visualization Cameras Planning Task analysis Neurons Collision avoidance Biology Brain-inspired structure collision-free movement planning small operational space spiking neural networks (SNNs)
DOI	10.1109/TNNLS.2021.3111051
关键词[WOS]	PREFRONTAL CORTEX ; AVOIDANCE ; NETWORK ; TIME
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[62073324] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404]
项目资助者	National Nature Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000732924300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46852
专题	复杂系统认知与决策实验室_听觉模型与认知计算
通讯作者	Zhang, Tielin
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Xing, Dengpeng,Li, Jiale,Zhang, Tielin,et al. A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:12.
APA	Xing, Dengpeng,Li, Jiale,Zhang, Tielin,&Xu, Bo.(2021).A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA	Xing, Dengpeng,et al."A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):12.