Flocking Controls for Swarms of Mobile Robots Inspired by Fish Schools

Abstract

Self-organizing and adaptive behaviors can be easily seen in flocks of birds or schools of fish. It is surprising that each individual member follows a small number of simple behavioral rules, resulting in sophisticated group behaviors (Wilson, 2000). For instance, when a school of fish is faced with an obstacle, they can avoid collision by being split into a plurality of smaller groups that can be merged after they pass around the obstacle. Based on the observation of such habits of schooling fishes, we propose collective navigation behavior rules that enable a large swarm of autonomous mobile robots to flock toward a stationary or moving goal in an unknown environment. Recently, robot swarms are expected to be deployed in a wide variety of applications such as odor localization, mobile sensor networking, medical operations, surveillance, and search-and-rescue (Sahin, 2005). In order to perform those tasks successfully, the behaviors of individual robots need to be controlled in a simple manner to support coordinated group behavior. Reynolds presented a distributed behavioral model of coordinated animal motion based on fish schools and bird flocks (Reynolds, 1987). His work demonstrated that navigation is an example of emergent behavior arising from simple rules. Many navigation strategies reported in the field of swarm robotics can be classified into centralized and decentralized strategies. Centralized strategies (Egerstedt & Hu, 2001) (Burgard et al, 2005) employ a central unit that organizes the behaviors of the whole swarm. This strategy usually lacks scalability and becomes technically unfeasible when a large swarm is considered. On the other hand, decentralized strategies are based on interactions between individual robots mostly inspired by evidence from biological systems or natural phenomena. Decentralized strategies can be further divided into biological emergence (Baldassarre et al, 2007) (Shimizu et al, 2006) (Folino & Spezzano, 2002), behavior-based (Ogren & Leonard, 2005) (Balch & Hybinette, 2000), and virtual physics-based (Spears et al, 2006) (Esposito & Dunbar, 2006) (Zarzhitsky et al, 2005) approaches. Specifically, the behavior-based and virtual physicsbased approaches are related to the use of such physical phenomena as crystallization (Balch & Hybinette, 2000) gravitational forces (Spears et al, 2005) (Zarzhitsky et al, 2005) (Spears et al, 2004) and potential fields (Esposito & Dunbar, 2006). Those works mostly use a force balance between inter-individual interactions exerting an attractive or repulsive force within the influence range, which might over-constrain the swarm and frequently lead to deadlocks. Moreover, the computations of relative velocities or accelerations between robots O pe n A cc es s D at ab as e w w w .in te hw eb .c om