Self-Organized Collective Systems using Implicit Coordination: Closing the perception gap between th
Abstract
Self-organized collectives represent a fascinating approach to autonomy, where seemingly purposeful coherent large-scale behavior em,erges from small scale interactions between many individuals -- so much so that the collective appears to be an autonomous entity by, itself. Understanding how to harness this method of autonomy has broad implications for engineering human-designed systems to be re,silient and scalable. Fish schools have specialized to create massive and impressive levels of collective intelligence in underwater, environments, migrating long distances, efficiently searching for resources, and even forming dynamic shapes like bait balls to cap,ture prey. They provide an important existence proof that a high degree of collective autonomy can be achieved underwater, in spite,of the inherent sensory and communication limitations. To do so, they rely on implicit coordination: individual fish make decision,s based primarily on their visual observation of nearest neighbors and without explicit communication, elegantly bypassing the inher,ent challenges of underwater communication. While many groups are developing methods for above-ground robotic systems, relying on gl,obal positioning and wireless communication, the ability to engineer collective behavior underwater through fish-like implicit coord,ination has been neglected. As a result, theoretical studies of fish-inspired autonomy remain abstract and unvalidated from an engin,eering perspective. We propose to develop a fundamental understanding of self-organized 3D collectives that use implicit coordinatio,n, by utilizing an integrated algorithmic-experimental approach based on a novel 3D underwater robotic platform BlueSwarm. Our goal,is to close the gap between theory and implementation for bio-inspired implicit coordination collective behaviors, especially with r,egards to limitations on 3D perception in underwater environments. Our research has two main thrusts: (1) Experimental investigation, of four core self-organized behaviors: dispersion, alignment, dynamic formation, and collective capture, and (2) Development of gen,eralized mathematical models for reasoning about perception-limited implicit coordination. Underwater multi-robot systems have many,important potential applications: environmental monitoring, inspections of underwater infrastructure, and search-and-rescue operatio,ns. Through our research, we aim to advance the state of the art in fully autonomous 3D underwater swarms, as well as advance the th,eory and algorithmic understanding of realizable collective autonomy with implicit coordination.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 05, 2022
- Source ID
- N000142212222
Entities
People
- Radhika Nagpal
Organizations
- Office of Naval Research
- Trustees of Princeton University
- United States Navy