Enabling Collective Locomotion, Manipulation, and Navigation by Entangled Swarms of Soft Robots

This grant supports research to enable understanding and design of swarms of string-like soft robots, capable of moving around their environment independently, or of tangling together to operate as a collective. The project seeks to create programs for the individual robots to follow so that, when in their entangled state, they can collectively move in response to external stimuli and transport or manipulate objects. For example, a swarm of individual robots might separately search an area for a designated object. Upon locating the object, the robots could rapidly retrieve it by forming an entangled collective to pick up the object, encapsulate it in a ball, and roll to the desired destination. This ability to act separately over a wide area but to come together to gain additional functions when needed, could one day enable important new applications in environmental monitoring or security surveillance. This work is inspired by the remarkable behavior of California blackworms, which form entangled 'blobs' under certain environmental conditions, but can disentangle almost instantly when appropriate. Like the blackworm, the entangled robots will be designed to to adjust their collective mechanical properties, such as stiffness, through the responses of the individual components, potentially surpassing the known locomotion and manipulation capabilities of the biological organism. In addition to the contributions to robotics, this work will provide insights into prediction and control of material properties in other physically entangled systems, such as polymer networks. The work seeks to develop pneumatically actuated high-aspect-ratio elastomeric robots with integrated sensing, and implement methods by which entangled groups of such robots demonstrate locomotion with rolling and peristaltic gaits, transportation and manipulation of external objects, and navigation in constrained environments such as mazes. Robots look to possess sensory capabilities of light sensing, proprioception, and tactile/proximity sensing. Initial control policies for the entangled groups will be based on known principles underlying collective locomotion in the California blackworm. These policies seek to subsequently seed machine learning procedures to further refine the collective behaviors. The results will be used to characterize principles underlying collective motion primitives and the ways they can be composed. The hardware aspect of the work looks to advance the state of the art in fabrication of soft robots, pushing the limits of geometries that can be achieved, developing new forms of integrated sensing, and providing new strategies for achieving desired behaviors with inherently imprecise robots. The algorithmic aspect of the work looks to identify new methods for engineering emergent systems: designing and verifying dynamic systems of interacting agents, which reliably achieve collective goals while adapting to each other and to uncertain and changing environments. 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. NSF Award ID: 2431757 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Justin Werfel | Institution: Harvard University, CAMBRIDGE, MA | Award Amount: $600,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2431757 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2431757.html