Automated Conflict Resolution Utilizing Probability Collectives Optimizer

Rising manned air traffic and deployment of unmanned aerial vehicles in complex operations requires integration of innovative and autonomous conflict detection and resolution methods. In this paper, the task of conflict detection and resolution is defined as an optimization problem searching for a heading control for cooperating airplanes using communication. For the optimization task, an objective function integrates both collision penalties and efficiency criteria considering airplanes' objectives (waypoints). The probability collectives optimizer is used as a solver for the specified optimization task. This paper provides two different implementation approaches to the presented optimization-based collision avoidance: 1) a parallel computation using multiagent deployment among participating airplanes and 2) semicentralized computation using the process-integrated-mechanism architecture. Both implementations of the proposed algorithm were implemented and evaluated in a multiagent airspace test bed AGENTFLY. The quality of the solution is compared with a negotiation-based cooperative collision avoidance method - an iterative peer-to-peer algorithm.

[1]  David Sislák,et al.  Agent-Based Approach to Free-Flight Planning, Control, and Simulation , 2009, IEEE Intelligent Systems.

[2]  Joseph S. B. Mitchell,et al.  SYSTEM PERFORMANCE CHARACTERISTICS OF CENTRALIZED AND DECENTRALIZED AIR TRAFFIC SEPARATION STRATEGIES , 2001 .

[3]  Vivek Sarkar,et al.  The Jikes Research Virtual Machine project: Building an open-source research community , 2005, IBM Syst. J..

[4]  Milan Rollo,et al.  A -globe: Agent Development Platform with Inaccessibility and Mobility Support , 2005 .

[5]  James K. Archibald,et al.  A cooperative multi-agent approach to free flight , 2005, AAMAS '05.

[6]  S. Shankar Sastry,et al.  Generation of conflict resolution manoeuvres for air traffic management , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[7]  David H. Wolpert,et al.  Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics , 2004, ArXiv.

[8]  David K. Chin,et al.  Using airspace simulation to assess environmental improvements from free flight and CNS/ATM enhancements , 1999, WSC '99.

[9]  David H. Wolpert,et al.  Adaptive, distributed control of constrained multi-agent systems , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[10]  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).

[11]  Jiří Lažanský,et al.  Industrial applications of agent technologies , 2007 .

[12]  Niranjan Suri,et al.  A Game based Approach to Comparing Different Coordination Mechanisms , 2008 .

[13]  John Hayhurst THE FUTURE OF AIR TRAFFIC CONTROL , 2001 .

[14]  George J. Pappas,et al.  Noncooperative Conflict Resolution , 1997 .

[15]  Yiyuan Zhao,et al.  Free flight concept , 1997 .

[16]  Letizia Leonardi,et al.  Mobile JikesRVM: A framework to support transparent Java thread migration , 2008, Sci. Comput. Program..

[17]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[18]  David H. Wolpert,et al.  Flight Control with Distributed Efiectors , 2005 .

[19]  George J. Pappas,et al.  Conflict resolution for multi-agent hybrid systems , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[20]  Vladimír Marík,et al.  Industrial adoption of agent-based technologies , 2005, IEEE Intelligent Systems.

[21]  Manolis A. Christodoulou,et al.  Automatic commercial aircraft-collision avoidance in free flight: the three-dimensional problem , 2006, IEEE Transactions on Intelligent Transportation Systems.

[22]  David Sislák,et al.  Decentralized algorithms for collision avoidance in airspace , 2008, AAMAS.

[23]  David H. Wolpert,et al.  Distributed control by Lagrangian steepest descent , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[24]  Sai-Ming Li,et al.  Forest fire monitoring with multiple small UAVs , 2005, Proceedings of the 2005, American Control Conference, 2005..

[25]  David H. Wolpert,et al.  Product distribution theory for control of multi-agent systems , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[26]  Kagan Tumer,et al.  Improving Air Traffic Management with a Learning Multiagent System , 2009, IEEE Intelligent Systems.

[27]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[28]  M. Pechoucek,et al.  Agent-Based Multi-Layer Collision Avoidance to Unmanned Aerial Vehicles , 2007, 2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems.

[29]  Ilan Kroo,et al.  Distributed optimization and flight control using collectives , 2005 .

[30]  A. Rollett,et al.  The Monte Carlo Method , 2004 .

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  Jeffrey M. Bradshaw,et al.  Strong Mobility and Fine-Grained Resource Control in NOMADS , 2000, ASA/MA.

[33]  Kenneth M. Ford,et al.  The PIM: an innovative robot coordination model based on Java thread migration , 2008, PPPJ '08.

[34]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[35]  Michael S. Nolan,et al.  Fundamentals of Air Traffic Control , 1990 .

[36]  Niranjan Suri State Capture and Resource Control for Java: The Design and Implementation of the Aroma Virtual Machine , 2001, Java Virtual Machine Research and Technology Symposium.

[37]  Yaneer Bar-Yam,et al.  Dynamics Of Complex Systems , 2019 .

[38]  Albert A. Groenwold,et al.  OPTIMAL SIZING DESIGN OF TRUSS STRUCTURES USING THE PARTICLE SWARM OPTIMIZATION ALGORITHM , 2002 .