DECENTRALIZED MULTI-ROBOT SLAM AND AD HOC NETWORK FOR EXPLORATION IN A REMOTE AND ENCLOSED ENVIRONMENT

Simple item page
dc.contributor.advisor Baek, Kyungim
dc.contributor.author Idota, Tetsuya
dc.contributor.department Computer Science
dc.date.accessioned 2022-07-05T19:58:25Z
dc.date.available 2022-07-05T19:58:25Z
dc.date.issued 2022
dc.description.degree Ph.D.
dc.identifier.uri https://hdl.handle.net/10125/102212
dc.subject Computer science
dc.subject Artificial intelligence
dc.subject Robotics
dc.subject ad hoc network
dc.subject decentralization
dc.subject enclosed environment
dc.subject multi-robot SLAM
dc.title DECENTRALIZED MULTI-ROBOT SLAM AND AD HOC NETWORK FOR EXPLORATION IN A REMOTE AND ENCLOSED ENVIRONMENT
dc.type Thesis
dcterms.abstract Robotic exploration in an enclosed, unknown, and unstructured environment such as a cave is a challenging problem as the robots cannot directly communicate with any exterior facilities. In such an environment, the robots need to perform Simultaneous Localization and Mapping (SLAM) without any external supports, e.g., GPS, a prior map, wifi access points, and so on. To cope with this situation, the proposed method establishes a decentralized multi-robot system forming an ad hoc network. Each robot is deployed at the entrance of the environment and moves into deeper areas while maintaining the distances to its neighboring robots to locally communicate with each other. In this formation, they perform the decentralized cooperative localization and the distributed submap building. The decentralized cooperative localization enables the robots to localize themselves with respect to the global reference frame by taking mutual measurements on a relative pose between the robots. Communicating with neighboring robots, each robot updates its estimated location based on sensory data about relative poses with respect to the neighbors and their estimated locations. To deal with the overconfidence problem, which leads to inconsistent estimates due to cyclic updates, the proposed method performs the conservative data exchange. When the robots pass their estimation to each other, they reduce the confidence in their estimation by applying fractional exponents to the probability distributions. As a result, the distributions become "smoother" with less intense peaks. Thereby, the robots can avoid inconsistent amplification of confidence and update their estimation based on each other's information without the knowledge about the entire network topology. The distributed submap building directs the robots to cooperatively build submaps, each of which represents a small local area in the environment. Each robot builds a series of submaps along its trajectory. When the robot detects that its neighboring robots enter the areas where submaps were built previously and can no longer update the submaps by itself, it passes the submaps to such robots so that the submaps can be used by the receiving robots. Thus, each of the robots does not have to hold all the submaps that it has created from the beginning of the operation, and hence can save the memory space for the mapping process. Consequently, the robots can deliver consistent results of mapping and localization in a decentralized manner while holding only the submaps associated with their local areas. The proposed methods are implemented in Robot Operating System (ROS) and the protocols for the communication are also designed. For experiments, demonstration, and evaluation, the implemented system is simulated using the Gazebo simulator.
dcterms.extent 157 pages
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.type Text
local.identifier.alturi http://dissertations.umi.com/hawii:11260
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Idota_hawii_0085A_11260.pdf
Size:
4.06 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Ph.D. - Computer Science