Control of robotic swarm behaviors based on smoothed particle hydrodynamics

The paper presents a fluid dynamics based framework for control of emergent behaviors of robot swarms that are modeled as fluids. A distributed low-level control mechanism is developed based on Smoothed Particle Hydrodynamics (SPH) and it is coupled with a high-level control layer that is responsible for the control of fluid parameters to generate desired behaviors from the swarming characteristics of the robots. It is shown by simulations that using the same low-level SPH model, different swarming behaviors can emerge from the local interactions of robots according to the settings of the fluid parameters that are controlled by the high-level control layer.

[1]  Luca Maria Gambardella,et al.  c ○ 2004 Kluwer Academic Publishers. Manufactured in The Netherlands. Swarm-Bot: A New Distributed Robotic Concept , 2022 .

[2]  Didier Keymeulen,et al.  Self-organizing system for the motion planning of mobile robots , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[3]  Tucker R. Balch,et al.  Communication, Diversity and Learning: Cornerstones of Swarm Behavior , 2004, Swarm Robotics.

[4]  Ismet Erkmen,et al.  Scalable Self-Deployment of Mobile Sensor Networks: A Fluid Dynamics Approach , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Alain Liégeois,et al.  Near Optimal Robust Path Planning for Mobile Robots: the Viscous Fluid Method with Friction , 2000, J. Intell. Robotic Syst..

[6]  Diana SPEARS,et al.  Physics-Based Robot Swarms For Coverage Problems , .

[7]  William M. Spears,et al.  Distributed, Physics-Based Control of Swarms of Vehicles , 2004 .

[8]  Guirong Liu,et al.  Smoothed Particle Hydrodynamics: A Meshfree Particle Method , 2003 .

[9]  C. Pozrikidis,et al.  Fluid Dynamics: Theory, Computation, and Numerical Simulation , 2001 .

[10]  D. Birchall,et al.  Computational Fluid Dynamics , 2020, Radial Flow Turbocompressors.

[11]  Kamal K. Gupta,et al.  An Incremental Harmonic Function-based Probabilistic Roadmap Approach to Robot Path Planning , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Suranga Hettiarachchi,et al.  An Overview of Physicomimetics , 2004, Swarm Robotics.

[13]  Mathieu Desbrun,et al.  Smoothed particles: a new paradigm for animating highly deformable bodies , 1996 .

[14]  Ismet Erkmen,et al.  Towards Fluent Sensor Networks: A Scalable and Robust Self-Deployment Approach , 2006, First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06).

[15]  Gerardo Beni,et al.  From Swarm Intelligence to Swarm Robotics , 2004, Swarm Robotics.

[16]  Erol Sahin,et al.  Swarm Robotics: From Sources of Inspiration to Domains of Application , 2004, Swarm Robotics.

[17]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[18]  Gaurav S. Sukhatme,et al.  Mobile Sensor Network Deployment using Potential Fields : A Distributed , Scalable Solution to the Area Coverage Problem , 2002 .

[19]  Seiichi Shin,et al.  Decentralized Control of Autonomous Swarm Systems Using Artificial Potential Functions: Analytical Design Guidelines , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[20]  Pradeep K. Khosla,et al.  Real-time obstacle avoidance using harmonic potential functions , 1991, IEEE Trans. Robotics Autom..