Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control

This paper presents a novel approach to collision and obstacle avoidance in fixed-wing unmanned aerial systems (UASs), vehicles with high speed and high inertia, operating in proximal or congested settings. A unique reformulation of classical artificial potential field (APF) navigational approaches, adaptively morphing the functions' shape considering six-degrees-of-freedom (6DOF) dynamic characteristics and constraints of fixed-wing aircraft, is fitted to an online predictive and prioritized waypoint planning algorithm for generation of evasive paths during abrupt encounters. The time-varying waypoint horizons output from the navigation unit are integrated into a combined guidance and nonlinear model predictive control scheme. Real-time avoidance capabilities are demonstrated in full nonlinear 6DOF simulation of a large unmanned aircraft showcasing evasion efficiency with respect to classical methods and collision free operation in a congested urban scenario.

[1]  Gonzalo Garcia,et al.  DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS , 2013 .

[2]  David R. Jacques,et al.  Flight test results for UAVs using boid guidance algorithms , 2012, CSER.

[3]  Ling Shi,et al.  Virtual attractive-repulsive potentials for cooperative control of second order dynamic vehicles on the Caltech MVWT , 2005, Proceedings of the 2005, American Control Conference, 2005..

[4]  René van Paassen,et al.  Artificial Force Field for Haptic Feedback in UAV Teleoperation , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Tyler Andrews,et al.  Computation Time Comparison Between Matlab and C++ Using Launch Windows , 2012 .

[6]  Shahriar Keshmiri,et al.  Advanced H-Infinity Trainer Autopilot , 2010 .

[7]  Derek James Bennet,et al.  Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles , 2011 .

[8]  Jonathan P. How,et al.  A New Nonlinear Guidance Logic for Trajectory Tracking , 2004 .

[9]  Shahriar Keshmiri,et al.  Multichannel sense-and-avoid radar for small UAVs , 2013 .

[10]  Shahriar Keshmiri,et al.  Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS , 2013 .

[11]  Matthew Keeter,et al.  Cooperative search with autonomous vehicles in a 3D aquatic testbed , 2012, 2012 American Control Conference (ACC).

[12]  Jonathan P. How,et al.  Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method , 2007 .

[13]  Shahriar Keshmiri,et al.  Nonlinear Model Predictive Controller for Navigation, Guidance and Control of a Fixed-Wing UAV , 2011 .

[14]  James K. Archibald,et al.  A Satisficing Approach to Aircraft Conflict Resolution , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Andrea L. Bertozzi,et al.  Multi-Vehicle Flocking: Scalability of Cooperative Control Algorithms using Pairwise Potentials , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[16]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[17]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[18]  Jonathan P. How,et al.  UAV Trajectory Design Using Total Field Collision Avoidance , 2003 .

[19]  Debasish Ghose,et al.  A Reactive Inverse PN algorithm for collision avoidance among multiple Unmanned Aerial Vehicles , 2009, 2009 American Control Conference.

[20]  Joel W. Burdick,et al.  Artificial potential functions for highway driving with collision avoidance , 2008, 2008 IEEE International Conference on Robotics and Automation.

[21]  Francesco Borrelli,et al.  Decentralized Receding Horizon Control and Coordination of Autonomous Vehicle Formations , 2008, IEEE Transactions on Control Systems Technology.

[22]  G. Gowtham,et al.  Simulation of multi UAV flight formation , 2005, 24th Digital Avionics Systems Conference.

[23]  S. Shankar Sastry,et al.  Decentralized nonlinear model predictive control of multiple flying robots , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

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

[25]  Kevin L. Moore,et al.  Flight demonstrations of unmanned aerial vehicle swarming concepts , 2006 .

[26]  B. Crowther,et al.  Flocking of autonomous unmanned air vehicles , 2003, The Aeronautical Journal (1968).

[27]  R. Olfati-Saber,et al.  Collision avoidance for multiple agent systems , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[28]  Xiao Fan Wang,et al.  Flocking of multi-agents with a virtual leader part II: with a virtual leader of varying velocity , 2007, 2007 46th IEEE Conference on Decision and Control.

[29]  H. Jin Kim,et al.  Nonlinear Model Predictive Formation Flight , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[30]  Xiao Fan Wang,et al.  Flocking of multi-agents with a virtual leader part I: with a minority of informed agents , 2007, 2007 46th IEEE Conference on Decision and Control.

[31]  Eric W. Frew,et al.  Sampling Severe Local Storms and Related Phenomena: Using Unmanned Aircraft Systems , 2012, IEEE Robotics & Automation Magazine.

[32]  Timothy W. McLain,et al.  Vector Field Path Following for Miniature Air Vehicles , 2007, IEEE Transactions on Robotics.

[33]  Naomi Ehrich Leonard,et al.  Virtual leaders, artificial potentials and coordinated control of groups , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).