Editorial: Swarm neuro-robots with the bio-inspired environmental perception

Abstract

From disaster zone exploration to environmental monitoring, robots capable of navigating complex and unpredictable environments are in high demand. Inspired by the efficiency of insect swarms, the field of neuro-robotics has seen breakthroughs in efficient environmental perception and interaction. This research topic, titled "Swarm Neuro-Robots with Bio-Inspired Environmental Perception", presents a cutting-edge exploration of insect-inspired neural structures and mechanisms in neuro-robots. The topic brings together six pioneering papers, each contributing unique insights into the development and application of neuro-robots with biologically inspired perception systems. From the innovative "Mobip" model that leverages MobileNet for driving perception, to the intricate study of neural feedback in motion detection, each paper highlights the exceptional adaptability and effectiveness of neuro-robots in research domains such as swarm robotics, bionic robotics, and unmanned systems. The overarching aim of this topic is not only to showcase the advancements in neuro-robotic technologies but also to deepen our understanding of how these biologically inspired systems can revolutionize practical applications in fields like search and rescue, smart transportation, and beyond. Parmar et al. proposed a novel study aiming at investigating the process of iterative learning during the reaching tasks. Their experiments reveal how our neuro-motor system recalibrates movements based on visual errors, favoring a simple, first-order model with a constant learning rate. The forgetting effects (error increasing) were observed in the unpracticed movement directions with learning effects from generalization from the practiced movement direction. This work provides a fresh approach to training in randomized movement sequences, supported by model analysis. This has notable implications for enhancing skill retention and adaptability in fields like sports coaching, neurorehabilitation, and human-machine interactions. This work is instrumental in guiding the development of more efficient and adaptable neuro-robotic systems.Ling et al. take a deep dive into the world of insect vision, specifically how they spot tiny targets against chaotic backgrounds. Inspired by this, they construct a computational model: small target motion detection(STMD) using neural feedback. The authors employ Schauder's fixed point theorem and the contraction mapping theorem to analyze the feedback constant, as well as the existence and uniqueness of solutions within the nonlinear dynamical systems created by their feedback loop. Using a novel time-delay feedback STMD model, the study offers a robust analytical framework for the feedback mechanism found in STMD-based neural circuits. This work is pivotal in bridging biological insights with neural and robotic studies.Ayali et al. proposed an innovative approach to hybrid bio-robotic research, addressing collective behavior across robotics and biology. They pinpoint a crucial gap in studying controlled collective motion using both real insects and robots. In response, the authors unveil the Nymbot-Locust bio-hybrid swarm – a pioneering platform where live locusts and custom-designed 'Nymbot' robots interact in a lab setting. This allows for controlled studies of both natural and artificial swarm dynamics. By examining biological and synthetic agents simultaneously, the research promises vital insights into complex swarm interactions, showcasing the immense potential of bio-robotic collaborations in understanding natural phenomena.Mikami et al. take inspiration from the remarkable adaptability of tubificine worm blobs to develop a simple yet effective agent-based model. Through careful observation of real worms (aggregation, responses to stimuli, movement in confined spaces), they captured the worms' ability to deform, twist, and move as a collective. In their model, each worm is represented as a flexible cross-shaped agent, and int