Abstract
We present a safety validation approach for Sense and Avoid (SAA) algorithms aboard Unmanned Aerial Vehicles (UAVs). We build multi-agent simulations to provide a test arena for UAVs with various SAA algorithms, in order to explore potential conflict situations. The simulation is configured by a series of parameters, which define a huge input space. Evolutionary search is used to explore the input space and to guide the simulation towards challenging situations, thus accelerating the process of finding dangerous faults of SAA algorithms and supporting the safety validation process. We applied our approach to the recently published Selective Velocity Obstacles (SVO) algorithm. In our first experiment, we used both random and evolutionary search to find mid-air collisions where UAVs have perfect sensing ability. We found evolutionary search can find some faults (here, interesting problems with SVO) that random search takes a long time to find. Our second experiment added sensor noise to the model. Random search found similar problems as it did in experiment one, but the evolutionary search found some interesting new problems. The two experiments show that the proposed approach has potential for safety validation of SAA algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Federal Aviation Administration, U.S. Department of Transportaion: Integration of Civil Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Roadmap, 1st edn (2013)
Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine 4, 23–33 (1997)
Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research 17, 760–772 (1998)
Jenie, Y.I., Van Kampen, E.-J., de Visser, C.C., Chu, Q.P.: Selective Velocity Obstacle Method for Cooperative Autonomous Collision Avoidance System for Unmanned Aerial Vehicles. In: AIAA Guidance, Navigation, and Control (GNC) Conference. American Institute of Aeronautics and Astronautics (2013)
Kochenderfer, M.J., Chryssanthacopoulos, J.: Robust airborne collision avoidance through dynamic programming. Massachusetts Institute of Technology, Lincoln Laboratory, Project Report ATC-371 (2011)
Temizer, S., Kochenderfer, M.J., Kaelbling, L.P., Lozano-Pérez, T., Kuchar, J.K.: Collision avoidance for unmanned aircraft using Markov decision processes. In: AIAA Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics (2010)
Arnold, J., Alexander, R.: Testing Autonomous Robot Control Software Using Procedural Content Generation. In: Bitsch, F., Guiochet, J., Kaâniche, M. (eds.) SAFECOMP. LNCS, vol. 8153, pp. 33–44. Springer, Heidelberg (2013)
McMinn, P.: Search-based software test data generation: A survey. Software Testing, Verification and reliability 14, 105–156 (2004)
Clegg, K., Alexander, R.: The discovery and quantification of risk in high dimensional search spaces. In: Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion, pp. 175–176. ACM (2013)
Alam, S., Lokan, C., Abbass, H.: What can make an airspace unsafe? characterizing collision risk using multi-objective optimization. In: IEEE Congress on Evolutionary Computation, CEC, pp. 1–8 (2012)
Alam, S., Lokan, C., Aldis, G., Barry, S., Butcher, R., Abbass, H.: Systemic identification of airspace collision risk tipping points using an evolutionary multi-objective scenario-based methodology. Transportation Research Part C: Emerging Technologies 35, 57–84 (2013)
Federal Aviation Administration: Federal Aviation Regulations (FAR) Chapter I, subchapter F Air Traffic and General Operating Rules, 91.113 Right-of-way rules: Except water operations (1989)
Federal Aviation Administration: JO 7110.65U, Air Traffic Control, Chapter 1: General. In: U.S. Department of Transportation (ed.) (2012)
Dubins, L.E.: On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. American Journal of Mathematics, 497–516 (1957)
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 24, 656–667 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zou, X., Alexander, R., McDermid, J. (2014). Safety Validation of Sense and Avoid Algorithms Using Simulation and Evolutionary Search. In: Bondavalli, A., Di Giandomenico, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2014. Lecture Notes in Computer Science, vol 8666. Springer, Cham. https://doi.org/10.1007/978-3-319-10506-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-10506-2_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10505-5
Online ISBN: 978-3-319-10506-2
eBook Packages: Computer ScienceComputer Science (R0)