A unified approach to cooperative and non-cooperative Sense-and-Avoid

Cooperative and non-cooperative Sense-and-Avoid (SAA) capabilities are key enablers for Unmanned Aircraft Vehicle (UAV) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative SAA sensor/system technologies for small-to-medium size UAV are identified and the associated multi-sensor data fusion techniques are introduced. A reference SAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting and sorting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the SAA system processes, the key mathematical models associated with both non-cooperative and cooperative SAA functions are presented. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are described. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative SAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UAV) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy SAA capability, which provides a pathway for manned/unmanned aircraft coexistence in all classes of airspace.

[1]  Daniel Hodouin,et al.  UAV Optimal Obstacle Avoidance while Respecting Target Arrival Specifications , 2011 .

[2]  Vitali Volovoi,et al.  Sense and Avoid Requirements for Unmanned Aircraft Systems Using a Target Level of Safety Approach , 2014, Risk analysis : an official publication of the Society for Risk Analysis.

[3]  Alessandro Gardi,et al.  Avionics sensor fusion for small size unmanned aircraft Sense-and-Avoid , 2014, 2014 IEEE Metrology for Aerospace (MetroAeroSpace).

[4]  Mandyam V. Srinivasan,et al.  Competent vision and navigation systems , 2009, IEEE Robotics & Automation Magazine.

[5]  Alessandro Gardi,et al.  Automated ATM system for 4-dimensional trajectory based operations , 2015 .

[6]  Anthony Finn,et al.  Acoustic sense & avoid for UAV's , 2011, 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

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

[8]  Giancarmine Fasano,et al.  Sense and avoid for unmanned aircraft systems , 2016, IEEE Aerospace and Electronic Systems Magazine.

[9]  Roberto Sabatini,et al.  Particle filter based multi-sensor data fusion techniques for RPAS navigation and guidance , 2015, 2015 IEEE Metrology for Aerospace (MetroAeroSpace).

[10]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[11]  David R. Maroney,et al.  UAS Sense and Avoid Development - the Challenges of Technology, Standards, and Certification , 2012 .

[12]  Roger D. Santer,et al.  Arousal facilitates collision avoidance mediated by a looming sensitive visual neuron in a flying locust. , 2008, Journal of neurophysiology.

[13]  Youmin Zhang,et al.  Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects , 2015 .

[14]  Michael D. Snyder,et al.  HUMAN FACTORS DESIGN GUIDE (HFDG) FOR ACQUISITION OF COMMERCIAL OFF-THE-SHELF SUBSYSTEMS, NON-DEVELOPMENTAL ITEMS, AND DEVELOPMENTAL SYSTEMS , 1996 .

[15]  Roberto Sabatini,et al.  Assessing GNSS integrity augmentation techniques in UAV sense-and-avoid architectures , 2015 .

[16]  Troy S. Bruggemann,et al.  Sensors for missions , 2015 .

[17]  L. Miller,et al.  How Some Insects Detect and Avoid Being Eaten by Bats: Tactics and Countertactics of Prey and Predator , 2001 .

[18]  Reece A. Clothier,et al.  Fixed-wing MAV attitude stability in atmospheric turbulence—Part 2: Investigating biologically-inspired sensors , 2014 .

[19]  Alessandro Gardi,et al.  A laser obstacle avoidance system for manned and unmanned aircraft detect-and-avoid , 2015 .

[20]  Mandyam V. Srinivasan,et al.  Competent Vision and Navigation Systems Competent Vision and Navigation Systems From Flying Insects to Autonomously Navigating Robots , 2009 .

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

[23]  Roberto Sabatini,et al.  Differential Global Positioning System (DGPS) for Flight Testing (Global Positioning System Differentiel (DGPS) pour les Essais en vol) , 2008 .

[24]  Alessandro Gardi,et al.  Towards a unified approach to cooperative and non-cooperative RPAS detect-and-avoid , 2014 .

[25]  Mark H. Draper,et al.  Human-Machine Interface Development for Common Airborne Sense and Avoid Program , 2014 .

[26]  Peter Thomas,et al.  On-board trajectory generation for collision avoidance in unmanned aerial vehicles , 2011, 2011 Aerospace Conference.

[27]  Patrick Garrec,et al.  Sense and avoid radar using Data Fusion with other sensors , 2011, 2011 Aerospace Conference.