Bio-Inspired Robotic Systems: Artificial Intelligence It Mimics Nature to be Efficient and Resilient

Abstract

Nature has been the most effective inspiration source from all times for engineering innovation when it comes to robotics. Bio-inspired robotics systems are the systems that combine the principles of various biological organisms with highly advanced Artificial Intelligence (AI) models in order to have the properties of adaptability, efficiency and resiliency in hostile environments. Unlike conventional machines, bio-inspired robots emulate the structural, functional and behavioral characteristics of living systems - such as the movement of insects, the sense of bats, and self-healing abilities of systems. Artificial Intelligence is the core of cognition which allows these robots to interpret the stimuli from the environment, dynamically adapt to the environment and make decisions on the basis of experience. Through the joining of biomechanics, neural computation and evolutionary algorithms has led to bio-inspired robotics being seen not as mechanical replication, but as a type of agent representing a new form of intelligent and self-organizing systems. This paper focuses on understanding how AI can be used to augment bio-inspired design, using both perception, learning, and decision-making as examples of processes occurring in natural systems. It further explores how the principles of nature, such as swarm intelligence, neural plasticity and morphological computation, can inform the development of robots that can operate in autonomy under non-predictable conditions. The integration of bio-inspired AI not only helps to provide optimisation of energy efficiency and environmental adaptation but also helps for robustness to system failure. This work shows that bio-inspired robotics is not a mere design paradigm, but a needed move to sustainable, intelligent, and resilient automation through an analysis of theoretical considerations, technological applications and real-life examples.