Flight Test Validation of Collision and Obstacle Avoidance in Fixed-Wing UASs with High Speeds Using Morphing Potential Field

A novel approach to collision and obstacle avoidance in fixed-wing unmanned aerial systems with high speed and high inertia was developed by reformulating classical artificial potential field navigational approaches. Classical artificial potential field navigation is a formidable approach to collision avoidance for slow and small robots including rotary-wing UASs, however they lack robustness and adaptability for large fixed-wing aircraft flying in close proximity or congested areas. As part of a concept demonstration, this work presents the validation and verification of morphing potential collision avoidance using large unmanned aerial systems flying at 60 ft/sec. The morphing potential function was constrained by the aircraft's six-degree-of-freedom dynamic characteristics and maximum allowable bank angle. A virtual time-varying waypoint is used to navigate the aircraft in a dynamically changing environment. The validation flight tests were successfully conducted and real-time avoidance capabilities were demonstrated.

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

[2]  Guo Zheng,et al.  An improved nonlinear guidance law for unmanned aerial vehicles path following , 2015, 2015 34th Chinese Control Conference (CCC).

[3]  Vijay Kumar,et al.  In-flight formation control for a team of fixed-wing aerial vehicles , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

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

[5]  P. B. Sujit,et al.  Adaptive Optimal Path Following for High Wind Flights , 2011 .

[6]  Jan Roskam,et al.  Airplane Flight Dynamics and Automatic Flight Controls , 2018 .

[7]  Heechul Yun,et al.  A Simplex Architecture for Intelligent and Safe Unmanned Aerial Vehicles , 2016, 2016 IEEE 22nd International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA).

[8]  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.

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

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

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

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

[13]  علی کریم پور,et al.  Flocking of Autonomous Unmanned Air Vehicles , 2006 .

[14]  Ian Sheppard,et al.  Dynamic Modeling and Simulation of A Quadcopter with Motor Dynamics , 2017 .

[15]  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).

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

[17]  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).

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

[19]  P. B. Sujit,et al.  Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless , 2014, IEEE Control Systems.

[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]  Michael S. Selig,et al.  Propeller Performance Data at Low Reynolds Numbers , 2011 .

[22]  Thomas Stastny,et al.  Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control , 2015 .

[23]  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).

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

[25]  Shahriar Keshmiri,et al.  Multichannel sense-and-avoid radar for small UAVs , 2013, 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC).

[26]  Matt R. Jardin,et al.  Optimized Measurements of Unmanned-Air-Vehicle Mass Moment of Inertia with a Bifilar Pendulum , 2009 .

[27]  Jianhua Zhang,et al.  UAV formation control based on consistency , 2015, 2015 7th International Conference on Modelling, Identification and Control (ICMIC).