Locomotion is controlled through interactions between the nervous system and mechanics of an animal’s body. This control is hierarchical, with local networks of neurons handling rapid reflex actions and brain centers serving to navigate an animal with respect to environmental sensory cues. In many animals, such as in swimming fish and running centipedes, local controllers coordinate with one another rather than depending solely on instructions from the animal’s brain; this coordination is reminiscent of how animals in a group coordinate their collective behavior. The supported research aims to develop theory on how these local interactions guide animal locomotion. This work will focus on sea stars, which use hundreds of tube feet to navigate through rough marine terrain with a distributed nervous system and no brain. The research will combine experimentation, mathematical modeling, and machine learning to understand how the mechanics of the tube feet and body govern the hierarchical control of locomotion. A general theory for locomotor control has the potential for applications in numerous areas of biological and engineering research. This work will enhance our understanding of how diverse animals control locomotion and will generate novel paradigms for the design of soft robots. The proposal will support science and engineering training opportunities for under-represented minorities. It will develop software that will be made freely available to the community of scientists and engineers. The investigators will additionally engage K-12 citizen-scientists on research projects that will be based at a public aquarium.
Animal locomotion is mediated in part by local nervous controllers that interact with one another in a manner similar to the collective behavior of animal groups. The supported research will develop the theory of collective neuromechanical control through experimentation and mathematical modeling focused on sea stars. The arrays of tube feet used by these animals provide sensing, integration, and actuation that is distributed throughout the body. This work will be organized around lines of investigation that seek to (1) understand how tube feet are controlled locally, (2) examine how sea stars climb and run, and (3) investigate how sea stars navigate with respect to light and gravity. Throughout, we will use reinforcement learning to formulate hypothetical control laws that we will test experimentally. Collective neuromechanical control offers great potential to influence our understanding of animal locomotion and to inspire the development of engineered devices. The tube feet of sea stars can inform knowledge of the neuromuscular control of other biological as well as soft robotic systems. The proposed work is highly interdisciplinary, with all components requiring interdependent efforts in engineering and biology.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
