Deep learning of structured environments for robot search

Caley, Jeffrey A.; Lawrance, Nicholas; Hollinger, Geoffrey A.

doi:10.1109/iros.2016.7759587

Cited by 20 publications

(1 citation statement)

References 13 publications

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“…Several methods predict the presence of specific elements in the unobserved parts of environments using neural networks trained on similar environments. For instance, [23] trains a convolutional neural network (CNN) on a set of images representing building floor plans and uses it to predict the locations of emergency exits. In [24], U-nets, a type of CNNs, are used to expand egocentric RGB-D observations to infer the occupancy state beyond the visible regions.…”

Section: Related Workmentioning

confidence: 99%

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

Luperto

Amadelli²,

Berardino³

et al. 2023

Robotics and Autonomous Systems

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Section: Related Workmentioning

confidence: 99%

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

Luperto

Amadelli²,

Berardino³

et al. 2023

Robotics and Autonomous Systems

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Deep learning of structured environments for robot search

2019

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Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

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Deep learning of structured environments for robot search

Cited by 20 publications

References 13 publications

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

Deep learning of structured environments for robot search

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

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