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Communication-Constrained STL Task Decomposition through Convex Optimization (2402.17585v1)

Published 27 Feb 2024 in eess.SY and cs.SY

Abstract: In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph. Specifically, we propose to decompose tasks defined over multiple agents which require multi-hop communication, by a set of sub-tasks defined over the states of agents with 1-hop distance over the communication graph. To this end, we parameterize the predicates of the tasks to be decomposed as suitable hyper-rectangles. Then, we show that by solving a constrained convex optimization, optimal parameters maximising the volume of the predicate's super-level sets can be computed for the decomposed tasks. In addition, we provide a formal definition of conflicting conjunctions of tasks for the considered STL fragment and a formal procedure to exclude such conjunctions from the solution set of possible decompositions. The proposed approach is demonstrated through simulations.

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References (20)
  1. D. Sun, J. Chen, S. Mitra, and C. Fan, “Multi-agent motion planning from signal temporal logic specifications,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3451–3458, 2022.
  2. F. S. Barbosa, D. Duberg, P. Jensfelt, and J. Tumova, “Guiding autonomous exploration with signal temporal logic,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3332–3339, 2019.
  3. L. Lindemann and D. V. Dimarogonas, “Control barrier functions for signal temporal logic tasks,” IEEE control systems letters, vol. 3, no. 1, pp. 96–101, 2018.
  4. L. Lindemann and D. V. Dimarogonas, “Control barrier functions for multi-agent systems under conflicting local signal temporal logic tasks,” IEEE Control Systems Letters, vol. 3, no. 3, pp. 757–762, 2019.
  5. L. Lindemann, C. K. Verginis, and D. V. Dimarogonas, “Prescribed performance control for signal temporal logic specifications,” in 56th IEEE Conference on Decision and Control, pp. 2997–3002, 2017.
  6. F. Chen and D. V. Dimarogonas, “Distributed control of coupled leader-follower multi-agent systems under spatiotemporal logic tasks,” in IFAC World Congress, 2023.
  7. N. Mehdipour, C.-I. Vasile, and C. Belta, “Arithmetic-geometric mean robustness for control from signal temporal logic specifications,” in 2019 American Control Conference (ACC), pp. 1690–1695.
  8. V. Raman, A. Donzé, M. Maasoumy, R. M. Murray, A. Sangiovanni-Vincentelli, and S. A. Seshia, “Model predictive control with signal temporal logic specifications,” in 53rd IEEE Conference on Decision and Control, pp. 81–87, 2014.
  9. Z. Liu, J. Dai, B. Wu, and H. Lin, “Communication-aware motion planning for multi-agent systems from signal temporal logic specifications,” in 2017 American Control Conference (ACC), pp. 2516–2521.
  10. G. A. Cardona, D. Kamale, and C.-I. Vasile, “Mixed integer linear programming approach for control synthesis with weighted signal temporal logic,” in Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control, pp. 1–12, 2023.
  11. M. Charitidou and D. V. Dimarogonas, “Signal temporal logic task decomposition via convex optimization,” IEEE Control Systems Letters, vol. 6, pp. 1238–1243, 2021.
  12. K. Leahy, A. Jones, and C.-I. Vasile, “Fast decomposition of temporal logic specifications for heterogeneous teams,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2297–2304, 2022.
  13. K. Leahy, M. Mann, and C.-I. Vasile, “Rewrite-based decomposition of signal temporal logic specifications,” in NASA Formal Methods Symposium, pp. 224–240, Springer, 2023.
  14. O. Maler and D. Nickovic, “Monitoring temporal properties of continuous signals,” in International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems, pp. 152–166, Springer, 2004.
  15. S. Sadraddini and C. Belta, “Robust temporal logic model predictive control,” in 53rd Annual Allerton Conference on Communication, Control, and Computing, pp. 772–779, IEEE, 2015.
  16. S. S. Farahani, V. Raman, and R. M. Murray, “Robust model predictive control for signal temporal logic synthesis,” IFAC, vol. 48, no. 27, pp. 323–328, 2015.
  17. V. Raman, A. Donzé, D. Sadigh, R. M. Murray, and S. A. Seshia, “Reactive synthesis from signal temporal logic specifications,” in Proceedings of the 18th international conference on hybrid systems: Computation and control, pp. 239–248, 2015.
  18. S. M. LaValle, Planning algorithms. Cambridge university press, 2006.
  19. Springer Science & Business Media, 2012.
  20. S. Froitzheim, Efficient conversion of geometric state set representations for hybrid systems. PhD thesis, Bachelor’s thesis. RWTH Aachen University, 2016.
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