Distributed formation control for swarm robots using mobile agents

This paper presents a decentralized control algorithm for composing a specific formation of swarm robots. The swarm robots are expected to compose formations that represent symbols. The robots are connected by communication networks and not necessary intelligent; they initially do not have any control program to compose symbols. Control programs that implement our algorithm are introduced later from outside as mobile software agents, which are able to migrate from one robot to another robot connected by the network. Our controlling algorithm is based on the indirect pheromone communication of social insects such as ants. We have implemented the ant and the pheromone as mobile software agents. Ant agents control the robots. Each ant agent has partial information about the formation they are supposed to compose. The partial information consists of relative locations with neighbor robots that are cooperatively composing the target formation. Once the ant agent detects an idle robot, it occupies that robot and generates the pheromone agent to attract other ant agents to the location for neighbor robots. Then the pheromone agent repeatedly migrates to other robots to diffuse attracting information. Once the pheromone agent reaches the robot with an ant agent, the ant agent migrates to the robot closest to the location pointed by the pheromone agent, and then, drives the robot to the location. We have implemented a simulator based on our algorithm, and conducted experiments to demonstrate the feasibility of our approach.

[1]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[2]  Yasushi Kambayashi,et al.  Ant Colony Clustering Using Mobile Agents as Ants and Pheromone , 2010, ACIIDS.

[3]  Kar-Han Tan,et al.  High Precision Formation Control of Mobile Robots Using Virtual Structures , 1997, Auton. Robots.

[4]  Yasushi Kambayashi,et al.  Searching Targets Using Mobile Agents in a Large Scale Multi-robot Environment , 2011, KES-AMSTA.

[5]  Yasushi Kambayashi,et al.  Suppressing Energy Consumption of Transportation Robots using Mobile Agents , 2013, ICAART.

[6]  Kiattisin Kanjanawanishkul,et al.  Formation Control of Mobile Robots: Survey , 2016 .

[7]  Baldo Faieta,et al.  Diversity and adaptation in populations of clustering ants , 1994 .

[8]  Walter Binder,et al.  Portable resource control in the J-SEAL2 mobile agent system , 2001, AGENTS '01.

[9]  Yasushi Kambayashi,et al.  A Serialization Algorithm for Mobile Robots Using Mobile Agents with Distributed Ant Colony Clustering , 2011, KES.

[10]  Yasushi Kambayashi,et al.  Suppressing the Total Costs of Executing Tasks Using Mobile Agents , 2009, 2009 42nd Hawaii International Conference on System Sciences.

[11]  Yasushi Kambayashi,et al.  Synthesizing Pheromone Agents for Serialization in the Distributed Ant Colony Clustering , 2018, IJCCI.

[12]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[13]  Radhika Nagpal,et al.  Robust and Self-Repairing Formation Control for Swarms of Mobile Agents , 2005, AAAI.

[14]  Hisashi Yamamoto,et al.  Integrating Ant Colony Clustering Method to a Multi-Robot System Using Mobile Agents , 2009 .

[15]  Yasushi Kambayashi,et al.  Higher-Order Mobile Agents for Controlling Intelligent Robots , 2005, Int. J. Intell. Inf. Technol..

[16]  Yasushi Kambayashi,et al.  Saving Energy Consumption of Multi-robots Using Higher-Order Mobile Agents , 2007, KES-AMSTA.

[17]  Jean-Louis Deneubourg,et al.  The dynamics of collective sorting robot-like ants and ant-like robots , 1991 .

[18]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[19]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..