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Comparing different sampling-based motion planners in multiple configuration spaces
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Jämförelse av olika sampling-based motion planners i flera configuration spaces (Swedish)
Abstract [en]

A lot of tasks in our society can be automatized through the use of motion planners. One approach of motion planning is known as sampling-based motion planning. In order to find out strengths and weaknesses of different sampling-based motion planners, benchmarks were run where the memory usage, the length of solutions and the time to find a feasible solution were compared. Multiple configuration spaces were used in order to correlate the effectiveness of planners with the different traits of the configuration spaces. The results show that the Lazy-PRM* takes up less memory than the PRM, RRT and RRT*. The results also suggest that there is a correlation between the configuration space and which planner finds the best result within a certain timeframe.

Abstract [sv]

Det finns många processer och uppgifter i vårt samhälle som kan automatiseras med hjälp av motion planners. En kategori av motion planners är känd som sampling-based motion planning. För att undersöka styrkor och svagheter för olika sampling-based motion planners så kördes ett flertal benchmarks. I dessa benchmarks mättes hur mycket minne som togs upp, längden av lösningarna och hur lång tid det tog att finna en giltig lösning. Flera configuration spaces användes i syfte att finna om vissa algoritmer är mer lämpade för configuration spaces med särskilda egenskaper. Resultaten visade att Lazy-PRM* tar upp mindre minne än PRM, RRT och RRT*. Resultaten antyder också att det finns ett samband mellan configuration space och vilken planeringsalgoritm som hittar bäst resultat inom en särskild tidsram.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:191
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-229757OAI: oai:DiVA.org:kth-229757DiVA, id: diva2:1214377
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-06 Last updated: 2018-06-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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