Open AccessJournal Article10.2514/1.I010412

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

- 18 Apr 2016

- Journal of Aerospace Information Systems

- Vol. 13, Iss: 5, pp 191-203

TL;DR: A new approach to testing multi-UAV conflict resolution algorithms is presented, which describes pairwise encounters in three-dimensional space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several Pairwise encounters.

Abstract: A new approach to testing multi-UAV conflict resolution algorithms is presented. The problem is formulated as a multi-objective search problem with two objectives: finding air traffic encounters that 1) are able to reveal faults in conflict resolution algorithms and 2) are likely to happen in the real world. The method uses agent-based simulation and multi-objective search to automatically find encounters satisfying these objectives. It describes pairwise encounters in three-dimensional space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several pairwise encounters. The consequences of the encounters, given the conflict resolution algorithm, are explored using a fast-time agent-based simulator. To find encounters meeting the two objectives, a genetic algorithm approach is used. The method is applied to test ORCA-3D, a widely cited open-source multi-UAV conflict resolution algorithm, and the method’s performance is compared with ...

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Better selection of features14 may, in the future, make their method even more powerful.. The resulting encounters providing the starting points for further analysis of the conflict resolution algorithm, which will in turn allow algorithm developers and users to fully understand its limitations.. Thus in general, their approach has the potential to offer an effective means of validating, or at least determining some limits of, SAA algorithms, thereby helping developers and regulators decide whether these important algorithms can be deployed

Fig. 4 Encoding multi-UAV encounters in genomes for the use of the GA based multi-objective search.

Fig. 8 A simulated random encounter of 9 UAVs. No incident occurred.

Table 3 Bounding values for the parameters

Fig. 3 The simulation of a multi-UAV encounter generated by the parameters in Table 2.

Fig. 5 Illustration of the one-point genome cross-over operator.

Citations

Journal Article•10.1016/J.JSS.2017.10.031

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Galen E. Mullins, +3 more

- 01 Mar 2018

- Journal of Systems and Software

TL;DR: This method provides a test engineer with key insights into the software’s decision-making engine and how those decisions affect transitions between performance modes through adaptive, simulation-based testing of the autonomous system where each sample represents a simulated scenario.

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Proceedings Article•10.1109/ICRA.2017.7989173

Automated generation of diverse and challenging scenarios for test and evaluation of autonomous vehicles

Galen E. Mullins, +2 more

- 01 May 2017

TL;DR: This work proposes a novel method for generating test scenarios for a black box autonomous system that demonstrate critical transitions in its performance modes by running the autonomous system through full missions in a simulated environment and measuring performance based on high-level mission criteria.

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•Journal Article•10.1016/J.FUTURE.2019.04.051

Agent-based simulation of unmanned aerial vehicles in civilian applications: A systematic literature review and research directions

Yazan Mualla, +6 more

- 01 Nov 2019

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TL;DR: From the SLR results, key research directions are highlighted including problems related to autonomy, explainability, security, flight duration, integration within smart cities, regulations, and validation & verification of the UAV behavior.

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Directional optimal reciprocal collision avoidance

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- 01 Feb 2021

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TL;DR: The Optimal Reciprocal Collision Avoidance (ORCA) algorithm is modified to make it work for autonomous and decentralized collision avoidance for civil aircraft and direction selectivity of the algorithm enables the process of collision avoidance to comply with the unified rules.

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SASSI: Safety Analysis Using Simulation-Based Situation Coverage for Cobot Systems

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Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

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Citations

Adaptive generation of challenging scenarios for testing and evaluation of autonomous vehicles

Automated generation of diverse and challenging scenarios for test and evaluation of autonomous vehicles

Agent-based simulation of unmanned aerial vehicles in civilian applications: A systematic literature review and research directions

Directional optimal reciprocal collision avoidance

SASSI: Safety Analysis Using Simulation-Based Situation Coverage for Cobot Systems

References

Real-time obstacle avoidance for manipulators and mobile robots

A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II

Real-time obstacle avoidance for manipulators and mobile robots

Genetic Algorithms and Machine Learning

Tutorial on agent-based modelling and simulation

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