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CAREER: Co-Design of Networking and Decentralized Control to Enable Aerial Networks in an Uncertain Airspace

NSF Project Numbers and Links: 1714519, 1453722

Airborne networking, unlike the networking of fixed sensors, mobile devices, and slowly-moving vehicles, is very challenging because of the high mobility, stringent safety requirements, and uncertain airspace environment. Airborne networking is important because of the growing complexity of the National Airspace System with the integration of unmanned aerial vehicles (UAVs). This project develops an innovative new theoretical framework for cyber-physical systems (CPS) to enable airborne networking, which utilizes direct flight-to-to-flight communication for flexible information sharing, safe maneuvering, and coordination of time-critical missions.

The Core: Aerial RMMs chart

This project uses an innovative co-design approach that exploits the mutual benefits of networking and decentralized mobility control in an uncertain heterogeneous environment. The approach departs from the usual perspective that views physical mobility as communication constraints, communication as constraints for decentralized mobility control, and uncertain environment as constraints for both. Instead, approach taken here proactively exploits the constraints, uncertainty, and new structures with information to enable high-performance designs. The features of the co-design such as scalability, fast response, trackability, and robustness to uncertainty advance the core CPS science on decision-making for large-scale networks under uncertainty.

The technological advances developed in this research will contribute to multiple fields, including mobile networking, decentralized control, experiment design, and general real-time decision making under uncertainty for CPS. Technology transfer will be pursued through close collaboration with industries and national laboratories. This novel research direction will also serve as a unique backdrop to inspire the CPS workforce. New teaching materials will benefit the future CPS workforce by equipping them with a knowledge base in networking and control. Broad outreach and dissemination activities that involve undergraduate student societies, K-12 school teaching, and public events, all stemming from the PI’s current efforts, will be enhanced.

Key Results

  • I. UAV Radom Mobility Models
  • II. Uncertainty-exploited Optimal Control
  • III. Structure-exploited Networking
  • IV. Multi-UAV Applications and Testbed Development

I. UAV Random Mobility Models

Random mobility models capture the random movement patterns of mobile agents, and have commonly served as the evaluation and design foundations of mobile networks. Existing random mobility models such as random direction, random walk, and random way-point were developed for ground-based platforms, and cannot capture the mobility patterns of UAVs. This study aims to develop random mobility models that are simple but accurately capture the mobility patterns of UAVs for UAV networking studies. Specific results include the smooth turn random mobility models for fixed-wing flights, the enhanced random mobility models with sense-and-avoid protocols to capture safety constraints, and the associated estimation methods for generic random mobility models.

UAV Random Mobility ModelsReference

  1. M. Liu, Y. Wan, F. L. Lewis, E. Atkins and D. Wu, “Statistical Properties and Airspace Capacity for Unmanned Aerial Vehicle Networks subject to Sense-and-Avoid Safety Protocols,” IEEE Transactions Intelligent Transportation Systems, vol. 22, no. 9, pp. 5890-5903, September 2021.
  2. T. Li, Y. Wan, M. Liu, and F. L. Lewis, “Estimation of Random Mobility Models using the Expectation-Maximization Method,” in Proceedings of the 14th IEEE International Conference on Control & Automation, ICCA 2018, Anchorage, Alaska, June 12-15, 2018.
  3. J. Xie, Y. Wan, B. Wang, S. Fu, and J. H. Kim, “A Comprehensive 3-Dimensional Random Mobility Modeling Framework for Airborne Networks,” IEEE Access, vol. 6, pp. 22849 – 22862, March 2018. Matlab Code for Z-Independent RMM     Matlab Code for Z-Dependent RMM
  4. Y. Zhou and Y. Wan, Smooth turn mobility model mablab code, AVAILABLE at: Link
  5. M. Liu and Y. Wan, “Analysis of Random Mobility Models with Sense and Avoid Protocols for UAS Capacity Management,” in Proceedings of AIAA SciTech Conference, Florida, January 2018.
  6. M. Liu, Y. Wan, and F. Lewis, “Analysis of the Random Direction Random Mobility Model with A Sense-and-Avoid Protocol, in Proceedings of Wi-UAV Workshop, Globecom, December 2017.
  7. J. Xie, Y. Wan, J. H. Kim, S. Fu, and K. Namuduri, “A survey and analysis of mobility models for airborne networks”, IEEE communications Surveys and Tutorials, pp. 1221-1238 (18 pages), vol. 16, no. 3, Third Quarter, 2014.
  8. J. Xie, Y. Wan, K. Namuduri, S. Fu, G. L. Peterson, and J. F. Raquet, “Estimation and Validation of the 3D Smooth-Turn Mobility Model for Airborne Networks”, in Proceedings of IEEE Military Communication Conference, November 2013.
  9. Y. Wan, K. Namduri, Y. Zhou, and S. Fu, “A smooth turn mobility model for airborne networks,” IEEE Transactions on Vehicular Technology, vol. 62, no. 7, pp. 3359-3370, September 2013.
  10. Y. Wan, K. Namduri, Y. Zhou, D. He, and S. Fu, “A Smooth Turn Mobility Model for Airborne Networks”, ACM Mobihoc Conference, June 2012.

II. Uncertainty-exploited Optimal Control

UAVs operate in environment of high-dimensional uncertainties. A critical step in the optimal control involves the evaluation of system output statistics under such uncertainties. Monte Carlo simulation and its variants have been used. However, they run into high computational load due to the nonlinear modulation of high dimensional uncertainties. We developed the method called M-PCM-OFFD, which integrates the Multivariate Probabilistic Collocation Method (PCM) and Orthogonal Fractional Factorial Design (OFFD) procedures to achieve an accurate, robust, and scalable uncertainty evaluation performance. We further used M-PCM-OFFD to solve practical optimal control and reinforcement-learning based control problems.

Uncertainty-exploited Optimal Control

Reference

  1. V. Kyriakos, Y. Wan, F. L. Lewis, and Derya H. Cansever, Handbook of Reinforcement Learning and Control, Springer, 2021.
  2. M. Liu, Y. Wan, Z. Lin, F. L. Lewis, J. Xie, and B. A. Jalaian, “Computational Intelligence in Uncertainty Quantification for Learning Control and Differential Games,” Book Chapter, Handbook of Reinforcement Learning and Control, Springer, pp. 315-418, June 2021.
  3. M. Liu, Y. Wan, F. Lewis, and V. Lopez, “Adaptive Optimal Control for Stochastic Multi-Agent Differential Games using On-Policy and Off-Policy Reinforcement Learning,” IEEE Transactions Neural Networks and Learning Systems, vol. 31, no. 12, pp. 5522-5533, December 2020.
  4. J. Xie, Y. Wan, Y. Zhou, K. Mills, J. J. Filliben, and Y. Lei, Effective Uncertainty Evaluation in Large-Scale, Principles of Cyber-Physical Systems: An Interdisciplinary Approach, Cambridge University Press, pp. 25-50, 2020.
  5. V. G. Lopez, Y. Wan, and F. L. Lewis, “Bayesian Graphical Games for Synchronization in Dynamical Networks,” IEEE Transactions on Control of Network Systems, vol. 7, no. 2, pp. 1028-1039, June 2020.
  6. M. Liu, Y. Wan, and F. Lewis, “Adaptive Optimal Decision in Multi-Agent Random Switching Systems,” IEEE Control Systems Letters, vol. 4, no. 2, pp.265-270, April 2020.
  7. M. Liu, Y. Wan, S. Li, F. Lewis, “Learning and Uncertainty-Exploited Directional Antenna Control for Robust Aerial Networking,” in Proceedings of IEEE Vehicle Technology Conference, accepted, Honolulu, Hawaii, September 2019.
  8. M. Liu, Y. Wan, F. L. Lewis, V. G. Lopez Mejia, “Stochastic Two-Player Zero-Sum Learning Differential Games,” in Proceedings of the 15th IEEE International Conference on Control & Automation (ICCA), Edinburgh, Scotland, June 2019.
  9. J. Xie, Y. Wan, K. Mills, J. J. Filliben, Y. Lei, and Z. Lin, “M-PCM-OFFD: An Effective Output Statistics Estimation Method for Systems of High Dimensional Uncertainties Subject to Low-Order Parameter Interactions,” Mathematics and Computers in Simulation, Vol. 159, pp. 93-118, May 2019.
  10. V. G. Lopez, Y. Wan, F. L. Lewis, “Bayesian Graphical Games for Synchronization in Dynamical Systems,” in Proceedings of American Control Conference, Milwaukee, MI, June 2018.
  11. J. Xie, Y. Wan, and F. F. Lewis, “Strategic Air Traffic Management under Uncertainties using Scalable Sampling-based dynamic Programming and Q-learning Approaches,” in Proceedings of Asian Control Conference, Australia, December 2017.
  12. J. Xie, Y. Wan, K. Mills, J. J. Filliben, and F. Lewis, “A Scalable Sampling Method to High-dimensional Uncertainties for Optimal and Reinforcement Learning-based Controls,” IEEE Control Systems Letters, vol. 1, no. 1, pp. 98-103, July 2017.
  13. Y. Zhou, Y. Wan, S. Roy, C. Taylor, C. Wanke, and J. Xie, “Multivariate probabilistic collocation method for effective uncertainty evaluation with application to air traffic management”, IEEE Transactions on Systems, Man, and Cybernetics, pp. 1347-1363 (17 pages), vol. 44, no. 10, October 2014.

III. Structure-exploited Networking

In typical distributed control, egalitarian networks are typically studied, where 1) agents are of the same functionality and no organization or hierarchy exists among these egalitarian agents, 2) the data from neighbors are immediately transmitted and available at any time instance. We find that layered structures are more effective than equivalent egalitarian structures in terms of the data transmission load required to reach consensus. We establish explicit relationships between simple structural characteristics and the performance of quantized consensus (e.g., consensus condition, consensus value, and transmission load to reach consensus) for broad classes of layered structures.

Structure-exploited Networking

Reference

  1. Y. Wan, J. Yan, Z. Lin, V. Sheth, S. Das, “On the Structural Perspective of Computational Effectiveness for Quantized Consensus in Layered UAV Networks,” IEEE Transactions on Control of Network Systems, vol. 6, no. 1, pp. 276-288, March 2019.
  2. L. Yao, Y. Wan, S. Fu, and T. Yang, “Consensus in Layered Sensor Networks with Communication Delays,” in Proceedings of 15th International Conference on Control, Automation, Robotics and Vision, Singapore, November 18-21, 2018.
  3. V. Sheth, Y. Wan, J. Xie, S. Fu, Z. Lin, S. K. Das, “On properties of quantized consensus in multi-layer multi-group sensor networks,” in Proceedings of IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina Del Rey, CA, May 26-28, 2014.
  4. Y. Wan, K. Numuduri, S. Akula, and M. Varanasi, “The impact of multi-group multi-layer network structure on the performance of distributed consensus building strategies”, International Journal of Robust and Nonlinear Control, vol. 23, issue 6, pp. 653-662, April 2013.
  5. Y. Wan, K. Namuduri, S. Akula, and M. Varanasi, “Consensus Building in Distributed Sensor Networks with Bipartite Graph Structures”, AIAA Guidance, Navigation, and Control Conference, August, Oregon, 2011.

IV. Multi-UAV Applications and Testbed Development

UAV networking has broad applications to infrastructure health monitoring, next-generation UAV traffic control, IoT mobility applications, and on-demand communication infrastructure for emergency response. We explore a flexible, cost-effective, and UAV-carried broadband long-distance communication infrastructure and investigate its capability for immediate use in emergency response.

Multi-UAV Applications and Testbed Development

Reference

  1. M. Liu, Y. Wan, S. Li, F. Lewis, and S. Fu, “Learning and Uncertainty-exploited Directional Antenna Control for Robust Long-distance and Broad-band Aerial Communication,” IEEE Transactions on Vehicular Technologies, vol. 69, no. 1, pp. 593-606, January 2020.
  2. S. Li, Y. Gu, B. Subedi, C. He, and Y. Wan, “Beyond Visual Line of Sight UAV Control for Remote Monitoring using Directional Antennas,” in Proceedings of IEEE GLOBECOM 2019 Workshop on Computing-Centric Drone Networks, Waikoloa, Hawaii, December 2019.
  3. S. Li, C. He, M. Liu, Y. Wan, Y. Gu, J. Xie, S. Fu, and K. Lu, “The Design and Implementation of Aerial Communication Using Directional Antennas: Learning Control in Unknown Communication Environment,” IET Control Theory and Application, vol. 13, no. 17, pp. 2906-2916, November 2019.
  4. M. Liu, Y. Wan, S. Li, F. Lewis, “Learning and Uncertainty-Exploited Directional Antenna Control for Robust Aerial Networking,” in Proceedings of IEEE Vehicle Technology Conference, Honolulu, Hawaii, September 2019.
  5. J. Chen, J. Xie, Y. Gu, S. Li, S. Fu, Y. Wan, and K. Lu, “Long-Range and Broadband Aerial Communication Using Directional Antennas (ACDA): Design and Implementation”, IEEE Transactions on Vehicular Technology, Vol. 66, No. 12, pp. 10893-10805, December 2017.
  6. J. Yan, Y. Wan, S. Fu, J. Xie, S. Li, and K. Lu, “Received signal strength indicator-based decentralised control for robust long-range aerial networking using directional antennas”, IET Control Theory and Applications, vol. 11, no. 11, pp. 1838-1847, July 2017.
  7. J. Yan, Y. Wan, J. Xie, S. Fu, “RSSI-based Heading Control for Robust Long-range Aerial Networking using Directional Antennas”, in Proceedings of American Control Conference, Seattle, WA, May 2017.
  8. J. Xie, F. Al-Emrani, Y. Gu, Y. Wan and S. Fu, “UAV-Carried Long Distance Wi-Fi Communication Infrastructure”, in Proceedings of AIAA Science and Technology Forum and Exposition, San Diego, January 2016.
  9. Y. Gu, M. Zhou, S. Fu, and Y. Wan, “Airborne WiFi Networks through Directional Antennae: An Experimental Study”, in Proceedings of 2015 IEEE Wireless Communications and Networking Conference, March 2015. Best Paper Award.

Other Publications

  1. “Networked UAVs for Disaster Response”, Unleashing autonomous drones for disaster risk reduction in AI FOR GOOD Webinars organized by International Telecommunication Union (ITU), Switzerland, October 2022. Link
  2. Research Outreach, Controlling UAV networks in uncertain environments. Link
  3. AIAA Intelligent Systems Technical Committee, Recommendations for Intelligent Systems in Aerospace: An AIAA/ISTC Position Statement, December 2017.
  4. AIAA Intelligent Systems Technical Committee, Roadmap for Intelligent Systems in Aerospace, June 2016.
  5. IEEE Access Special Issue on Networks of Unmanned Aerial Vehicles: Wireless Communications, Applications, Control and Modelling, co-edited by L. G. Giordano, A. Karimoddini, M. Magarini, G. Parr, M. S. Prasad, Y. Wan.  Link