Flight Test Validation of Adaptive Collision Avoidance Algorithms Using Multiple Unmanned Aircraft

With the advent of the unmanned aerial systems (UAS) era, it is important that research be conducted into the reliable and safe operation of UAS in urban areas, where aircraft are flying over people and property in spatially constrained environments. A large focus in recent years has been on adaptable collision avoidance systems capable of avoiding fixed and airborne traffic. This paper presents flight test validation of an adaptive collision algorithm known as the morphing potential field algorithm. The morphing potential field algorithm has been modified to allow implementation of more advanced guidance logics such as $L_{2}^{+}$ guidance. The aim is to validate the morphing collision avoidance path planning algorithm in a complex scenario, with multiple aircraft–two fixed wing and two rotary wing–flying the same spatially constrained area. The aircraft were intentionally put in a collision course with each other and other obstacles. Validation flight tests were successfully conducted to assess performance of morphing potential field navigation algorithms and also to quantify the impact of possible communication delays on the safety of collision avoidance methods for high speed and high inertia aircraft.

[1]  Paul Y. Oh,et al.  Optic-Flow-Based Collision Avoidance , 2008, IEEE Robotics & Automation Magazine.

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

[3]  Pu Zhang,et al.  Fixed-Wing UAV Formation Control Design With Collision Avoidance Based on an Improved Artificial Potential Field , 2018, IEEE Access.

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

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

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

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

[8]  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).

[9]  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).

[10]  Fredrik Gustafsson,et al.  Probabilistic Noncooperative Near Mid-Air Collision Avoidance , 2011, IEEE Transactions on Aerospace and Electronic Systems.

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

[12]  Shawn Keshmiri,et al.  Flight Test Validation of Collision and Obstacle Avoidance in Fixed-Wing UASs with High Speeds Using Morphing Potential Field , 2018, 2018 International Conference on Unmanned Aircraft Systems (ICUAS).

[13]  Gabriel Hugh Elkaim,et al.  L+2, an improved line of sight guidance law for UAVs , 2013, 2013 American Control Conference.

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

[15]  Shawn Keshmiri,et al.  SCALABILITY OF MESH NETWORK TELEMETRY FOR SWARMS OF UNMANNED AERIAL SYSTEMS , 2018 .

[16]  Youdan Kim,et al.  Stereo vision based collision avoidance of quadrotor UAV , 2012, 2012 12th International Conference on Control, Automation and Systems.

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

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

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

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

[21]  Shawn Keshmiri,et al.  Validation and Verification Flight Testing of UAS Morphing Potential Field Collision Avoidance Algorithms , 2018 .

[22]  Sally C. Johnson,et al.  Exploration of Detect-and-Avoid and Well-Clear Requirements for Small UAS Maneuvering in an Urban Environment , 2017 .

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

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

[25]  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).

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

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

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

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