Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance - Liu, Junxiu; Hua, Yifan; Yang, Rixing; Luo, Yuling; Lu, Hao; Wang, Yanhu; Yang, Su; Ding, Xuemei (Frontiers in Neuroscience, 2022)
Spiking Neural Networks (SNNs) are often considered as the third generation of Artificial Neural Networks (ANNs), owing to high information processing capability and the accurate simulation of biological neural network behaviours. Though the research for SNNs has been quite active in recent years, there are still some challenges to apply SNNs to various potential applications, especially for the robot control. In this work, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario.