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Multi-Robot Exploration for Improved Information Collection in Unknown Environments Under Communications Constraints.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multi-Robot Exploration for Improved Information Collection in Unknown Environments Under Communications Constraints./
作者:	Woosley, Bradley.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	123 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
標題:	Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28030889
ISBN:	9798684676192

Multi-Robot Exploration for Improved Information Collection in Unknown Environments Under Communications Constraints.
Woosley, Bradley.

Multi-Robot Exploration for Improved Information Collection in Unknown Environments Under Communications Constraints. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 123 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--University of Nebraska at Omaha, 2020.

This item must not be sold to any third party vendors.

Multi-robot exploration of initially unknown environments is an important problem in many applications of robots including autonomous surveillance, unmanned search and rescue, extra-terrestrial exploration, and detection of hazardous phenomena such as chemical or toxic gas emissions. In this dissertation, we make four contributions towards making multi-robot exploration more adaptable to real-life settings: a) an intelligent exploration algorithm enables robots to integrate information and communication availability in the environment while selecting locations to explore, b) a novel algorithm that combines geometric approximation of regions explored by robots with a fast, tree-based, information caching technique to resolve conflicts between multiple robots that visit the same locations repeatedly, c) an approximation method for distance metric to improve the computation speed of selecting goal locations during the robots' exploration without significantly deteriorating the quality of collected information, and, d) a feasibility study into robots assuming another's mental state and track it down for rescue by a specialized robot. We have validated the proposed algorithms experimentally using both simulated and physical robots in different environment settings with different numbers of robots and different information distribution and communication availability profiles. Our results show that our proposed approaches can substantially improve the performance of multi-robot exploration under communication constraints: our information and communication integrated multi-robot exploration algorithm results in up to 25% reduction in distance to achieve a similar level of certainty of the mapped environment compared to only accounting for information gain; our region approximation and tree-based information caching algorithm for conflict resolution is able to resolve multi-robot conflicts with up to 62% fewer messages compared to a message flood approach; our utility approximation techniques provides an up to 7-fold improvement in the effectiveness of every second spent planning compared to our original method. Finally, our evaluation of assuming the robot's mental state shows promise for future exploration in developing robust algorithms to find missing robots.

ISBN: 9798684676192Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Alpha shapes

Multi-Robot Exploration for Improved Information Collection in Unknown Environments Under Communications Constraints.
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500 $a Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
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520 $a Multi-robot exploration of initially unknown environments is an important problem in many applications of robots including autonomous surveillance, unmanned search and rescue, extra-terrestrial exploration, and detection of hazardous phenomena such as chemical or toxic gas emissions. In this dissertation, we make four contributions towards making multi-robot exploration more adaptable to real-life settings: a) an intelligent exploration algorithm enables robots to integrate information and communication availability in the environment while selecting locations to explore, b) a novel algorithm that combines geometric approximation of regions explored by robots with a fast, tree-based, information caching technique to resolve conflicts between multiple robots that visit the same locations repeatedly, c) an approximation method for distance metric to improve the computation speed of selecting goal locations during the robots' exploration without significantly deteriorating the quality of collected information, and, d) a feasibility study into robots assuming another's mental state and track it down for rescue by a specialized robot. We have validated the proposed algorithms experimentally using both simulated and physical robots in different environment settings with different numbers of robots and different information distribution and communication availability profiles. Our results show that our proposed approaches can substantially improve the performance of multi-robot exploration under communication constraints: our information and communication integrated multi-robot exploration algorithm results in up to 25% reduction in distance to achieve a similar level of certainty of the mapped environment compared to only accounting for information gain; our region approximation and tree-based information caching algorithm for conflict resolution is able to resolve multi-robot conflicts with up to 62% fewer messages compared to a message flood approach; our utility approximation techniques provides an up to 7-fold improvement in the effectiveness of every second spent planning compared to our original method. Finally, our evaluation of assuming the robot's mental state shows promise for future exploration in developing robust algorithms to find missing robots.
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