Formation Control and Persistent Monitoring in the OpenUAV Swarm Simulator on the NSF CPS-VO

Lukina, Anna; Kumar, Arjun; Schmittle, Matt; Singh, Abhijeet; Das, Jnaneshwar; Rees, Stephen A.; Buskirk, Christopher P.; Sztipanovits, János; Grosu, Radu; Kumar, Vijay

doi:10.1109/iccps.2018.00050

Cited by 3 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1, a central agent will be needed to periodically compute the global cost and resize the neighborhood. Before deployment, we plan to use OpenUAV simulator [11] to test DAMPC on drone formation control scenarios described in [9].…”

Section: The Distributed Ampc Algorithmmentioning

confidence: 99%

Distributed adaptive-neighborhood control for stochastic reachability in multi-agent systems

Lukina

Tiwari

Smolka

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

We present DAMPC, a distributed, adaptive-horizon and adaptive-neighborhood algorithm for solving the stochastic reachability problem in multi-agent systems, in particular flocking modeled as a Markov decision process. At each time step, every agent calls a centralized, adaptive-horizon model-predictive control (AMPC) algorithm [13] to obtain an optimal solution for its local neighborhood. Second, the agents derive the flock-wide optimal solution through a sequence of consensus rounds. Third, the neighborhood is adaptively resized using a flock-wide, cost-based Lyapunov function V . This way DAMPC improves efficiency without compromising convergence. We evaluate DAMPC's performance using statistical model checking. Our results demonstrate that, compared to AMPC, DAMPC achieves considerable speed-up (two-fold in some cases) with only a slightly lower rate of convergence. The smaller average neighborhood size and lookahead horizon demonstrate the benefits of the DAMPC approach for stochastic reachability problems involving any controllable multi-agent system that possesses a cost function.

show abstract

Section: The Distributed Ampc Algorithmmentioning

confidence: 99%

Distributed adaptive-neighborhood control for stochastic reachability in multi-agent systems

Lukina

Tiwari

Smolka

et al. 2019

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Attendees will be able to experiment with model import, building, and analysis execution, discuss and view how to integrate their own tools, view the current status of last year's OpenUAV [1] demo, and explore our library of tools. A pair of screens would be beneficial, as would a strong wireless or wired Internet connection.…”

Section: Demonstrationmentioning

confidence: 99%

Cyber-physical systems virtual organization: Active resources

Rees

Kecskes

Meijer

et al. 2019

Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems

View full text Add to dashboard Cite

The Cyber-Physical Systems Virtual Organization (CPS-VO) 1 has been evolving from a shared repository of information into a destination for active collaboration, simulation, hands-on education, and demonstration. We would like to show-case advances in tool integration, particularly a set of verification tools, and how this integration enables reproducibility, improves accessibility, and lowers the barrier to entry in this field. We would also like to demonstrate use of our simulation and tool frameworks, have a poster showing results and progress over the last year, and invite others to host their tools on the CPS-VO infrastructure. CCS CONCEPTS • Computing methodologies → Modeling and simulation; • Software and its engineering → Software libraries and repositories; • Mathematics of computing → Mathematical software; • Information systems → Web applications; • Applied computing → Education.

show abstract