Nature-Inspired Robotic Neural System
Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Reenu Batra · 2024
Nature has always been a source of inspiration for robots, with biological systems demonstrating extraordinary efficiency and flexibility. This study goes into the field of nature-inspired robotics, with a particular emphasis on brain networks as the foundation of biomimetic devices. This research seeks to give an in-depth examination of neural system integration in robotics, addressing both biologically inspired artificial neural networks and the interface between natural and artificial neural systems. We investigate how neural networks might improve robotic capabilities in a variety of applications, ranging from autonomous navigation to human-robot interaction and beyond. Our study focuses on critical issues such as neural network modeling for robotic control, sensorimotor integration, and cognitive process emulation in robots. We want to find bottlenecks that are impeding the smooth integration of nature-inspired brain networks into robotics, and we want to find ways to bridge the gap between biological principles and practical application. The fundamental goal of this research is to improve our understanding of nature-inspired robotic neural networks in order to stimulate the creation of more efficient and adaptable robotic technology. Through the introduction of biologically inspired neural networks, we want to provide fresh ways for increasing the performance, resilience, and adaptability of robotic systems. We emphasize the accomplishments and problems in replicating natural brain networks through extensive study and observation. We also investigate the potential ethical and societal consequences of furthering nature-inspired robotics, highlighting the significance of responsible and ethical growth in this transformational subject.