Constraints on locational optimization problems

Salapaka, Srinivasa M.; Khalak, A.; Dahleh, Munther A.

doi:10.1109/cdc.2003.1272864

Cited by 37 publications

(37 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other works have investigated variations upon this control law [4], [5], however, in all of these works the robots are required to know a priori the distribution of sensory information in the environment. We previously relaxed this requirement by using a simple memoryless approximation from sensor measurements [6], though a stability proof was not found.…”

Section: A Relation To Previous Workmentioning

confidence: 99%

Consensus learning for distributed coverage control

Schwager¹,

Slotine²,

Rus³

2008

2008 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-A decentralized controller is presented that causes a network of robots to converge to a near optimal sensing configuration, while simultaneously learning the distribution of sensory information in the environment. A consensus (or flocking) term is introduced in the learning law to allow sharing of parameters among neighbors, greatly increasing learning convergence rates. Convergence and consensus is proven using a Lyapunov-type proof. The controller with parameter consensus is shown to perform better than the basic controller in numerical simulations.

show abstract

Section: A Relation To Previous Workmentioning

confidence: 99%

Consensus learning for distributed coverage control

Schwager¹,

Slotine²,

Rus³

2008

2008 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

show abstract

“…Also, the facts that u i is continuous ∀i, H has continuous first partial derivatives, H is radially unbounded, andḢ ≤ 0 imply thatḢ is uniformly continuous. Therefore, by Barbalat's lemma lim t→∞Ḣ = 0, which implies (8).…”

Section: Theorem 1 (Non-adaptive Ladybug Convergence)mentioning

confidence: 87%

“…It was shown in [1] that coverage control can be phrased as a locational optimization problem [7]. Many controllers using this paradigm have been proposed, for example [8]- [10]. Notably, in [8] a deterministic annealing approach was adapted to the locational optimization problem to find globally optimal solutions.…”

Section: A Relation To Previous Workmentioning

confidence: 99%

“…Many controllers using this paradigm have been proposed, for example [8]- [10]. Notably, in [8] a deterministic annealing approach was adapted to the locational optimization problem to find globally optimal solutions. The adaptive coverage controller presented in [6] combines techniques from adaptive control [11]- [13] with the controller in [1] to learn the distribution of sensory information in the environment.…”

Section: A Relation To Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

A ladybug exploration strategy for distributed adaptive coverage control

Schwager¹,

Bullo

Skelly

et al. 2008

2008 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-A control strategy inspired by the hunting tactics of ladybugs is presented to simultaneously achieve sensor coverage and exploration of an area with a group of networked robots. The controller is distributed in that it requires only information local to each robot, and adaptive in that it modifies its behavior based on information in the environment. The ladybug controller is developed as a modification to a basic coverage control law, first for the non-adaptive case, then for the adaptive case. Stability is proven for both cases with a Lyapunov-type proof. Results of numerical simulations are presented.

show abstract

“…For static problems, d(s i , r j ) is typically chosen to be the square Euclidean distance x 1i − x 3 j 2 [14], [20]- [23]. As a result, the resource locations y j from solutions of (2) are located at the 'centroid' of the static clusters of sites z i .…”

Section: B Metricmentioning

confidence: 99%

Dynamic Maximum Entropy algorithms for clustering and coverage control

Salapaka

Beck

2010

49th IEEE Conference on Decision and Control (CDC)

Self Cite

View full text Add to dashboard Cite

Abstract-The dynamic coverage problem is increasingly found in a wide variety of areas, for example, from the development of mobile sensor networks, to the analysis of clustering in spatio-temporal dynamics of brain signals. In this paper, we apply control-theoretic methods to locate and track cluster center dynamics and show that dynamic control design is necessary to achieve dynamic coverage of mobile objects under acceleration fields. This is the first work to consider tracking cluster centers when site dynamics involve accelerations. We focus on the relationship between the objective of maximizing coverage in real-time and the Maximum Entropy Principle, and develop the ability to identify inherent cluster dynamics in a dataset. Algorithms are presented that guarantee asymptotic tracking of cluster centers, and for which we prove continuity and boundedness of the corresponding control laws. Simulations are provided to corroborate these results.

show abstract

Constraints on locational optimization problems

Cited by 37 publications

References 9 publications

Consensus learning for distributed coverage control

Consensus learning for distributed coverage control

A ladybug exploration strategy for distributed adaptive coverage control

Dynamic Maximum Entropy algorithms for clustering and coverage control

Contact Info

Product

Resources

About