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Improving path planning of autonomous vacuum cleaners using obstacle classification
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
Förbättrad vägplanering för självgående robotdammsugare genom hinderklassificering (Swedish)
Abstract [en]

To effectively plan their movement, robotic vacuum cleaners require information about their surrounding environment. This thesis presents a mapping algorithm which uses collision input from a moving robot to create a grid map of the environment. The algorithm also classifies each obstacle cell in the grid map as either mobile or immobile.

Methods for creating maps of unknown environments already exist. However, they often rely on information from expensive sensory equipment, and the maps do not include information about the mobility of the objects in the map. Hence, the aim of this thesis was to investigate if the map could be created using only limited information from more accessible sensors.

A testing environment was created using the Unity 3D game engine, in which the robot was represented by a circular object. The robot had access to perfect information about its current position in relation to its starting position, the direction in which it was heading and any incoming collisions. Three test scenes were created in the simulation environment, representing different common operating spaces for a robotic vacuum cleaner. The scenes contained different kinds of mobile and immobile obstacles that the robot collided with.

A series of tests were then conducted in the test scenes. The tests examined the performance of the created algorithm. The results indicated that the algorithm can create a grid map representation of an unknown environment and classify objects within it with an average correctness of around 80%. However, it is hard to say whether the algorithm would be effective in a real situation, due to the inconsistent results and unrealistic assumptions.

Abstract [sv]

Autonoma robotdammsugare behöver information om den omgivande miljön för att kunna planera sin resväg på ett effektivt sätt. I den här rapporten presenteras en kartläggningsalgoritm som med hjälp av kollisions information skapar en rutnätskarta av den omgivande miljön. Algoritmen klassificerar även hindren i rutnätskartan som mobila eller immobila.

Det existerar redan metoder för att kartlägga ökända miljöer. Dock använder många av dessa metoder information från mer avancerade och dyrare sensorer, och kartorna innehåller ingen information om mobiliteten hos hindren i miljön. Syftet med denna rapport är därför att undersöka huruvida en sådan karta skulle kunna skapas med hjälp av mer tillgängliga och enklare sensorer.

En testmiljö skapades med hjälp av spelmotorn Unity 3D, och i denna miljö representerades roboten av ett cirkulärt objekt. Roboten hade tillgång till felfri information rörande sin position i relation till sin startposition, riktningen i vilken roboten rörde sig i, och alla inkommande kollisioner. Tre stycken olika testrum skapades, vilka representerade olika vanliga miljöer i vilka autonoma robotdammsugare brukar användas. Rummen innehöll olika typer av mobila och immobila hinder, vilka roboten kolliderade med.

En uppsättning av tester utfördes sedan i dessa testrum. Testerna undersökte den skapade algoritmens prestanda. Resultaten visade på att algoritmen kan skapa en rutnätskarta som representerar en okänd miljö, och klassificerar mobiliteten hos hindren med en genomsnittlig korrekthet på runt 80%. Dock är det svårt att avgöra huruvida algoritmen kan prestera lika bra i en verklig miljö, på grund av de inkonsekventa resultaten och de förmodligen orealistiska antagandena.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:188
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-229471OAI: oai:DiVA.org:kth-229471DiVA, id: diva2:1213402
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-04 Last updated: 2018-06-26Bibliographically approved

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