Human-Swarm Interactions Based on Managing Attractors
Abstract
Leveraging the abilities of multiple affordable robots as a swarm is enticing because of the resulting robustness and emergent behaviors of a swarm. However, because swarms are composed of many different agents, it is difficult for a human to influence the swarm by managing individual agents. Instead, we propose that human influence should focus on (a) managing the higher level attractors of the swarm system and (b) managing trade-offs that appear in mission relevant performance. We claim that managing attractors theoretically allows a human to abstract the details of individual agents and focus on managing the collective as a whole. Using a swarm model with two attractors, we demonstrate this concept by showing how limited human influence can cause the swarm to switch between attractors. We further claim that using quorum sensing allows a human to manage trade-offs between the scalability of interactions and mitigating the vulnerability of the swarm to agent failures.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 06, 2014
- Accession Number
- AD1003111
Entities
People
- Daniel S. Brown
- Michael A Goodrich
- Sean C. Kerman
Organizations
- Rome Laboratory