期刊
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II
卷 12397, 期 -, 页码 509-520出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-61616-8_41
关键词
Cognitive robotics; Neural motion control; Spiking Neural Networks; Wavefront algorithm
类别
资金
- Baden-Wurttemberg Stiftung
Safe, yet efficient, Human-robot interaction requires real-time-capable and flexible algorithms for robot control including the human as a dynamic obstacle. Even today, methods for collision-free motion planning are often computationally expensive, preventing real-time control. This leads to unnecessary standstills due to safety requirements. As nature solves navigation and motion control sophisticatedly, biologically motivated techniques based on the Wavefront algorithm have been previously applied successfully to path planning problems in 2D. In this work, we present an extension thereof using Spiking Neural Networks. The proposed network equals a topologically organized map of the work space, allowing an execution in 3D space. We tested our work on simulated environments with increasing complexity in 2D with different connection types. Subsequently, the application is extended to 3D spaces and the effectiveness and efficiency of the used approach are attested by simulations and comparison studies. Thereby, a foundation is set to control a robot arm flexibly in a workspace with a human co-worker. In combination with neuromorphic hardware this method will likely achieve real-time capability.
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