Sense and avoid technology developments at Queensland University of Technology

In recent years, the avoidance strategies have become progressively more complex, yet better aligned to pilot see and avoid behaviour. Significant advances have also allowed the system to be characterised by two mutual exclusive thresholds, one for making avoidance decisions and the other for determining when to stop avoidance behaviour. The importance of this is that existing performance evaluation techniques, used to asses systems, such as TCAS, can be leveraged to simultaneously optimise system parameters, determine performance limits, and visualise design trade-offs. The evaluation framework also follows on naturally from the techniques utilising receiver operating curves used to asses the detection performance using similar techniques.

[1]  Quan Pan,et al.  A vision based sense and avoid system for small unmanned helicopter , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[2]  Jason J. Ford,et al.  An infinite-horizon robust filter for uncertain hidden Markov models with conditional relative entropy constraints , 2011, 2011 Australian Control Conference.

[3]  S. W. Shaw,et al.  Efficient target tracking using dynamic programming , 1993 .

[4]  Tarak Gandhi,et al.  Detection of obstacles in the flight path of an aircraft , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Aaron Mcfadyen,et al.  Decision strategies for automated visual collision avoidance , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[6]  Michael Taylor,et al.  Sense and avoid technology for unmanned aircraft systems , 2007, SPIE Defense + Commercial Sensing.

[7]  Plamen Angelov Sense and Avoid in UAS : Research and Applications , 2012 .

[8]  Peter Corke,et al.  Visual servoing approach to collision avoidance for aircraft , 2012 .

[9]  Sanjiv Singh,et al.  Prototype Sense-and-Avoid System for UAVs , 2009 .

[10]  David Hyunchul Shim,et al.  Vision-based sense-and-avoid framework for unmanned aerial vehicles , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Aaron Mcfadyen,et al.  Design and evaluation of decision and control strategies for autonomous vision-based see and avoid systems , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[12]  Khristo N. Boyadzhiev,et al.  Spirals and Conchospirals in the Flight of Insects , 1999 .

[13]  Peter I. Corke,et al.  Visual Predictive Control of Spiral Motion , 2014, IEEE Transactions on Robotics.

[14]  Timothy Molloy,et al.  Short-data recursive HMM parameter estimation for rapid vision-based aircraft heading estimation , 2014, 2014 4th Australian Control Conference (AUCC).

[15]  Kimon P. Valavanis,et al.  On unmanned aircraft systems issues, challenges and operational restrictions preventing integration into the National Airspace System , 2008 .

[16]  Jason J. Ford,et al.  Relative Entropy Rate Based Multiple Hidden Markov Model Approximation , 2010, IEEE Transactions on Signal Processing.

[17]  Jason J. Ford,et al.  Fat and Thin Adaptive HMM Filters for Vision Based Detection of Moving Targets , 2011, ICRA 2011.

[18]  V. Aidala,et al.  Observability Criteria for Bearings-Only Target Motion Analysis , 1981, IEEE Transactions on Aerospace and Electronic Systems.

[19]  Timothy Molloy,et al.  HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection , 2012, 2012 15th International Conference on Information Fusion.

[20]  Giancarmine Fasano,et al.  Morphological filtering and target tracking for vision-based UAS sense and avoid , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[21]  Omid Shakernia,et al.  Passive Ranging for UAV Sense and Avoid Applications , 2005 .

[22]  Peter I. Corke,et al.  Rotorcraft collision avoidance using spherical image-based visual servoing and single point features , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Peter I. Corke,et al.  Image processing algorithms for UAV "sense and avoid" , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[24]  Jason J. Ford,et al.  Practical stability of approximating discrete-time filters with respect to model mismatch , 2012, Autom..

[25]  Giancarmine Fasano,et al.  Multi-Sensor-Based Fully Autonomous Non-Cooperative Collision Avoidance System for Unmanned Air Vehicles , 2008, J. Aerosp. Comput. Inf. Commun..

[26]  Yair Barniv,et al.  Dynamic Programming Solution for Detecting Dim Moving Targets , 1985, IEEE Transactions on Aerospace and Electronic Systems.

[27]  Peter I. Corke,et al.  Aircraft collision avoidance using spherical visual predictive control and single point features , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Tamas Zsedrovits,et al.  A novel algorithm for distant aircraft detection , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[29]  Jason J. Ford,et al.  See and Avoid Using Onboard Computer Vision , 2012 .

[30]  Michael Bosse,et al.  A study of morphological pre-processing approaches for Track-Before-Detect dim target detection , 2008, ICRA 2008.

[31]  Jason J. Ford,et al.  Characterization of Sky‐region Morphological‐temporal Airborne Collision Detection , 2013, J. Field Robotics.

[32]  Jason J. Ford,et al.  Hidden Markov Model Filter Banks for Dim Target Detection from Image Sequences , 2008, 2008 Digital Image Computing: Techniques and Applications.

[33]  Jason J. Ford,et al.  Fusion of morphological images for airborne target detection , 2012, 2012 15th International Conference on Information Fusion.

[34]  Jason J. Ford,et al.  Vision-based detection and tracking of aerial targets for UAV collision avoidance , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[35]  Brett R. Fajen,et al.  Intercepting moving targets: a little foresight helps a lot , 2009, Experimental Brain Research.

[36]  Jason J. Ford,et al.  Airborne vision‐based collision‐detection system , 2011, J. Field Robotics.

[37]  Jason J. Ford,et al.  Looming Aircraft Threats: Shape-based Passive Ranging of Aircraft from Monocular Vision , 2014, ICRA 2014.

[38]  C Craig Morris,et al.  Midair collisions: limitations of the see-and-avoid concept in civil aviation. , 2005, Aviation, space, and environmental medicine.

[39]  Luis Mejias,et al.  Omnidirectional bearing-only see-and-avoid for small aerial robots , 2011, The 5th International Conference on Automation, Robotics and Applications.

[40]  Jason J. Ford,et al.  Vision-Based Estimation of Airborne Target Pseudobearing Rate using Hidden Markov Model Filters , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[41]  Peter I. Corke,et al.  Spherical image-based visual servo and structure estimation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[42]  Jing-Jiang Yan,et al.  Visual processing of the impending collision of a looming object: time to collision revisited. , 2011, Journal of vision.

[43]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[44]  Aaron McFadyen Visual control for automated aircraft collision avoidance systems , 2015 .

[45]  Timothy Molloy,et al.  HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection , 2013, 2013 Australian Control Conference.