A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space
文献类型：期刊论文
| 作者 | Xing, Dengpeng1,2 ; Li, Jiale1,2; Zhang, Tielin1,2; Xu, Bo1,2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2021-09-13 |
| 页码 | 12 |
| 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) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2021.3111051 |
| 通讯作者 | Zhang, Tielin(tielin.zhang@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | PREFRONTAL CORTEX ; AVOIDANCE ; NETWORK ; TIME |
| 资助项目 | National Nature Science Foundation of China[62073324] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000732924300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Nature Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46851]  |
| 专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
| 通讯作者 | Zhang, Tielin |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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