A Fluid Dynamic Based Coordination of a Wireless Sensor Network of Unmanned Aerial Vehicles: 3-D Simulation and Wireless Communication Characterization

A fluid dynamic algorithm based on smoothed particle hydrodynamics (SPH) is proposed for coordination of a team of unmanned aerial vehicles (UAVs) in a wireless sensor network. SPH is a Lagrangian particle method typically used to model compressible and quasi-incompressible fluid flows. In this study, SPH is used to develop a decentralized controller for a swarm of fixed-wing UAVs, which move in 3-D space under constraints of airspeed and turning radius. Vector field path-following is used to guide the swarm towards the goal. We investigate circular, racetrack and counter-rotating loiter patterns for the UAVs in the goal region. This fluid dynamics coordination treatment allows UAVs to avoid collisions with obstacles and other flying UAVs. 3-D simulations are used to test the SPH-based control algorithm. Simulations were used to explore special cases, such as the modeling of obstacles with virtual SPH particles, and the use of a variable kernel to control the inter-vehicle separation. Finally, an aerial mobile sensor network is set up using SPH as the control mechanism, and an experimental characterization of air-to-air and air-to-ground communications is conducted. The experiments use two ground stations and three Delta-wing UAVs with a wingspan of 32 inches as nodes. Each node has a IEEE 802.15.4 ZigBee radio operating in the 2.4 GHz band. The low computational costs involved in the distributed SPH-based control algorithm make it an attractive option for implementation on simple inexpensive microprocessors. The results of simulations and experiments demonstrate the viability of setting up a mobile sensor network of inexpensive UAVs based on SPH.

[1]  R. Mesquita,et al.  Fluids in Electrostatic Fields: An Analogy for Multirobot Control , 2007, IEEE Transactions on Magnetics.

[2]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[3]  Dale A. Lawrence,et al.  Autonomous UAV Control Using a 3-Sensor Autopilot , 2007 .

[4]  Ahmad Bilal Hasan,et al.  SensorFlock: A Mobile System of Networked Micro-Air Vehicles ; CU-CS-1018-06 , 2006 .

[5]  Paul W. Cleary,et al.  Modelling confined multi-material heat and mass flows using SPH , 1998 .

[6]  Kamran Mohseni,et al.  Concentration Gradient and Inform ation Energy for Decentralized UAV Control 1 , 2006 .

[7]  Timothy W. McLain,et al.  Maximizing miniature aerial vehicles , 2006, IEEE Robotics & Automation Magazine.

[8]  Kamran Mohseni,et al.  Information Energy for Sensor-Reactive UAV Flock Control , 2004 .

[9]  Huafeng Liu,et al.  Meshfree particle method , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Naomi Ehrich Leonard,et al.  Collective Motion, Sensor Networks, and Ocean Sampling , 2007, Proceedings of the IEEE.

[11]  Peter Berczik Modeling the Star Formation in Galaxies Using the Chemo-DynamicalSPH Code , 2000 .

[12]  Paul W. Cleary,et al.  Flow modelling in casting processes , 2002 .

[13]  Vijay Kumar,et al.  Control of swarms based on Hydrodynamic models , 2008, 2008 IEEE International Conference on Robotics and Automation.

[14]  Eric W. Frew,et al.  Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control , 2008 .

[15]  Not Available Not Available Meshfree particle methods , 2000 .

[16]  Dale A. Lawrence,et al.  Autonomous Gust Insensitive Aircraft 1 , 2008 .

[17]  Janne Riihijärvi,et al.  Performance study of IEEE 802.15.4 using measurements and simulations , 2006, IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006..

[18]  David M. Fratantoni,et al.  Multi-AUV Control and Adaptive Sampling in Monterey Bay , 2006, IEEE Journal of Oceanic Engineering.

[19]  Timothy W. McLain,et al.  Multiple UAV cooperative search under collision avoidance and limited range communication constraints , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[20]  Kamran Mohseni,et al.  Cooperative Control of a Team of AUVs Using Smoothed Particle Hydrodynamics With Restricted Communication , 2009 .

[21]  J. Monaghan Smoothed particle hydrodynamics , 2005 .

[22]  Vijay Kumar,et al.  Closed loop motion planning and control for mobile robots in uncertain environments , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[23]  Myung J. Lee,et al.  A Comprehensive Performance Study of IEEE 802 . 15 . 4 , 2004 .

[24]  Bhaskar Krishnamachari,et al.  Performance evaluation of the IEEE 802.15.4 MAC for low-rate low-power wireless networks , 2004, IEEE International Conference on Performance, Computing, and Communications, 2004.