Research of Bio-Inspired Motion Control in Robotics

Abstract

Bio-inspired motion control in robotics draws inspiration from biological systems to enhance the movement capabilities of robots. This article explores the integration of bionics techniques in robots’ path planning, motion control, and design of moving parts, offering advantages over traditional robots control systems. In path planning, bio-inspired approaches, such as swarm intelligence algorithms and artificial neural networks, optimize trajectories and enable obstacle avoid ability in complex environments. Furthermore, bio-inspired design principles facilitate the creation of motion components tailored for specific locomotion modes, such as legged locomotion and aquatic propulsion, improving robots’ agility and adaptability. Reflex-based and vision-based control methods emulate biological responses and utilize visual sensors to enhance robots’ perception and responsiveness. Additionally, recent advancements include the exploration of Long Short-Term Memory Networks (LSTM) for predicting control inputs based on animal trajectories. Through a synthesis of biomechanical principles, materials science, and artificial intelligence integration, bio-inspired motion control revolutionizes robotic capabilities, with implications for autonomous navigation and task execution in dynamic environments. Future research directions include further investigation into biomechanical principles, advancements in materials science, and the integration of artificial intelligence algorithms for enhanced autonomy and adaptability in robotics.