2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) 2019
DOI: 10.1109/mrs.2019.8901079
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On Minimum Time Multi-Robot Planning with Guarantees on the Total Collected Reward

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Cited by 11 publications
(9 citation statements)
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“…Constraint (17) requires the path length to be within budget. Constraints in (18) allow coefficients to be non-zero only if the corresponding measurement is selected. Constraints in (19) ensure the path starts at node x 1 and ends at node x M .…”
Section: B Informative Path Planning (Ipp)mentioning
confidence: 99%
See 1 more Smart Citation
“…Constraint (17) requires the path length to be within budget. Constraints in (18) allow coefficients to be non-zero only if the corresponding measurement is selected. Constraints in (19) ensure the path starts at node x 1 and ends at node x M .…”
Section: B Informative Path Planning (Ipp)mentioning
confidence: 99%
“…Orienteering [17] is concerned with finding a budget constrained tour in a graph that maximizes the reward collected at vertices. A formulation where a certain reward must be collected in minimum time is considered in [18]. In contrast, our work considers a general cost function on a subset of vertices namely, the estimation error.…”
Section: Introductionmentioning
confidence: 99%
“…A similar approach [7] incorporates a Boustrophedon cell decomposition. A related problem for multiple UAVs to collect maximum rewards under battery constraints has been posed as the k-STROLL problem (NP-hard) [32].…”
Section: A Coverage and Energy-aware Coveragementioning
confidence: 99%
“…Potential applications of the proposed framework include persistent monitoring [24], [25], environmental data collection [23], and scene reconstruction [26]. The authors of [27], [28] propose an interactive framework for marine data collection: Users define desired targets for observation, the robot then proposes alternatives based on additional information about risks in the environment.…”
Section: Introductionmentioning
confidence: 99%