For example, people walking on the street, bats flying in the evening sky, ants busily working on the ground, and sardines migrating in swarms of billions–can we find a certain pattern or rule in animal movements, that form the basis of life activity? With a completely new research concept and approach, our collaborative team has taken a major step toward creating a set of technologies to understand, predict, and control the complex essence and diverse elements of biological navigation.
We define “navigation” as the individual-level movement of organisms (how to reach a destination), “interaction” as a mutual influence with other individuals and environments, which is a higher level of navigation, and “hierarchical navigation” as the act of individuals and groups reaching the destination in a hierarchical manner. Our first mission is to develop methods for measuring and analyzing animal/human behavior to elucidate the essential components of hierarchical navigation and their causal relations. Then, through the creation of a new discipline, “Hierarchical Bio-Navigation,” we can transform the methodology and technology to solve social problems concerning animal/human behavior. This is a challenging project that aims to reach a higher level, which conventional approaches have not been able to do so through interdisciplinary and intensive collaboration between biology, engineering, and informatics.
Our collaborative team has named the causal relationships among the essential components of hierarchical navigation (e.g., aim of movements, locomotion ability, navigational skills, physiological states such as hunger and stress, and interference and coordination with other individuals) as “Behavioral diagram.” For example, birds searching for food may navigate by using cues such as the sun and/or smell, following their congeners, and avoiding predators. However, it is difficult to determine which elements are important and how they interact with each other to control the behavior of individual birds.
The χ(chi) logbot (logbot: logging robot), a research platform that seamlessly combines cutting-edge measurements and analytical methods, tackles this difficult issue. Our original idea, χlogbot, is a robot that uses AI to autonomously control the measurement and intervention of behavior (experimental approach to the subject) and automates experiments to elucidate hierarchical navigation. By attaching χlogbots to animals or installing them in a laboratory or outdoor environments, we can automate the experimental cycle from measurements in physical space (real world) to analyze and select intervention strategies in cyberspace (on a computer). The key characteristic of χlogbot is that it analyzes and learns the causal relationships of the data collected by AI and decides how to conduct the experimental intervention to verify and reinforce the hypotheses in the behavioral diagram.
We call this novel methodology the “seamless Cyber-Physical System (CPS),” which realizes a framework for refining a behavioral diagram by repeatedly constructing and validating the mathematical and machine learning models.
The following main results are expected from the efforts of our collaborative team. (1) Through the innovative behavioral measurements such as “χlogbot,” we will be able to obtain information on hierarchical navigation of various organisms with high accuracy that has not been available before, leading to the understanding of animal migration. Why migratory birds do not get lost, why fish can move in a large swarm, and other mysteries related to the animal migration may be revealed.
(2) The new methodology, “seamless CPS,” will enable us to provide and verify various hierarchical navigation models and establish a foundation for analyzing information on the movement of things and organisms, including humans. We may find an unexpected similarity and diversity in the movement of organisms and things.
(3) By repeating the cycle of measurement, analysis, selection of intervention strategies, and intervention on χlogbots, we will synergistically advance the fields of biology, engineering, and information science. In the future, we intend to apply the results to a variety of social and engineering issues. There is a wide range of fields in which “Hierarchical Bio-Navigation” can be used; for instance, future technologies that are expected to be implemented in society, such as flying cars and fully autonomous driving, and for the coexistence between humans and wildlife. We are moving forward to explore the complex and profound themes of navigation.
