Efficient Routing Algorithms for Multiple Vehicles With no Explicit Communications

Arsie, Alessandro; Savla, Ketan; Frazzoli, Emilio

doi:10.1109/tac.2009.2028954

Cited by 88 publications

(82 citation statements)

References 31 publications

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“…Clearly there are other ways to implement the rule (18). For example, we could let all the robots travel to the event location at the same speed, as in [22], a scheme that does not require any coordination. Then only the first robot to reach the target changes its reference position for the next period.…”

Section: Adaptive Coverage Controlmentioning

confidence: 99%

“…Now consider the following optimization problem (22) (23) where is a given cost function. The following theorem gives a general way of producing assignments or partitions that optimize (22), (23 (22). The value of the optimization problem is equal to (24) and this maximum is attained for some .…”

Section: Adaptive Spatial Load-balancing and Partitioningmentioning

confidence: 99%

“…It is then natural to ask how to gradually improve the spatial configuration of the robotic network based only on these observations. Indeed, recently some coverage control algorithms [12], [21] and vehicle routing algorithms [18], [22] have been developed to work in the absence of a priori knowledge of the event location distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Finally in Section V we present the first fully adaptive algorithm for the DTRP. In light traffic conditions, the policy reduces to the coverage control algorithm of Section III, and is simpler than the previous algorithm presented in [22]. In heavy traffic conditions, it relies on the partitioning algorithm of Section IV.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Deployment of Mobile Robotic Networks

Pappas

2013

IEEE Trans. Automat. Contr.

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Abstract-This paper considers deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Moreover, it is assumed that the event location distribution is a priori unknown, and can only be progressively inferred from the observation of the actual event occurrences. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. In each case, distributed stochastic gradient algorithms optimizing the performance objective are presented. The stochastic gradient view simplifies and generalizes previously proposed solutions, and is applicable to new complex scenarios, such as adaptive coverage involving heterogeneous agents. Remarkably, these algorithms often take the form of simple distributed rules that could be implemented on resource-limited platforms.Index Terms-Adaptive algorithms, coverage control problems, dynamic vehicle routing problems, partitioning algorithms, potential field based motion planning, stochastic gradient descent algorithms.

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Section: Adaptive Coverage Controlmentioning

confidence: 99%

Section: Adaptive Spatial Load-balancing and Partitioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Deployment of Mobile Robotic Networks

Pappas

2013

IEEE Trans. Automat. Contr.

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“…Problems involving multiple vehicles and with minimal communication have been considered in [14]. In [15], adaptive and decentralized policies are developed for the multiple service vehicle versions, and in [16], a dynamic team-forming variation of the Dynamic Traveling Repairperson problem is addressed.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Vehicle Routing for Translating Demands: Stability Analysis and Receding-Horizon Policies

Bopardikar

Smith

Bullo

et al. 2010

IEEE Trans. Automat. Contr.

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We introduce a problem in which demands arrive stochastically on a line segment, and upon arrival, move with a fixed velocity perpendicular to the segment. We design a receding horizon service policy for a vehicle with speed greater than that of the demands, based on the translational minimum Hamiltonian path (TMHP). We consider Poisson demand arrivals, uniformly distributed along the segment. For a fixed segment width and fixed vehicle speed, the problem is governed by two parameters; the demand speed and the arrival rate. We establish a necessary condition on the arrival rate in terms of the demand speed for the existence of any stabilizing policy. We derive a sufficient condition on the arrival rate in terms of the demand speed that ensures stability of the TMHP-based policy. When the demand speed tends to the vehicle speed, every stabilizing policy must service the demands in the first-come-first-served (FCFS) order; and the TMHP-based policy becomes equivalent to the FCFS policy which minimizes the expected time before a demand is serviced. When the demand speed tends to zero, the sufficient condition on the arrival rate for stability of the TMHP-based policy is within a constant factor of the necessary condition for stability of any policy.Finally, when the arrival rate tends to zero for a fixed demand speed, the TMHP-based policy becomes equivalent to the FCFS policy which minimizes the expected time before a demand is serviced. We numerically validate our analysis and empirically characterize the region in the parameter space for which the TMHP-based policy is stable.

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Adaptive control and signal processing literature survey (No. 16)

2010

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk.

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Efficient Routing Algorithms for Multiple Vehicles With no Explicit Communications

Cited by 88 publications

References 31 publications

Adaptive Deployment of Mobile Robotic Networks

Adaptive Deployment of Mobile Robotic Networks

Dynamic Vehicle Routing for Translating Demands: Stability Analysis and Receding-Horizon Policies

Adaptive control and signal processing literature survey (No. 16)

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