Robot Task Planning on Multiple Environments

Paulo Pinheiro, Jacques Wainer, Eleri Cardozo
<span title="">2014</span> <i title="International Academy Publishing (IAP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7wdo44rafbg5rfskrbd6snu7yy" style="color: black;">International Journal of Computer and Electrical Engineering</a> </i> &nbsp;
This paper presents a hierarchical planning approach to solving the global localization problem for mobile robots using a compaction map method. This approach uses architectural design features such as walls and doors to break the environment into rooms and cluster them by similarity constraints. Each group of similar rooms is mixed into a single, compact representative map. Lighter POMDP plans are generated only for these compact maps and not for the whole environment, decreasing size of the
more &raquo; ... t of possible states. In the higher layer of the planner, the robots use the compact maps to define which group of rooms they are located in. In the lower layer, the specific room of a representative map is isolated and its probability distribution is used to find out the precise localization. We demonstrate the hierarchical approach and the new map compaction with experiments on both V-REP Simulator and a Pioneer 3DX robot. This approach allows the robot to perform both the localization and tasking in a large-scale environment while keeping the accuracy.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17706/ijcee.2014.v6.863">doi:10.17706/ijcee.2014.v6.863</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r6ho5nfun5capebqg6kefulnwe">fatcat:r6ho5nfun5capebqg6kefulnwe</a> </span>
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