Improving Robotic Vacuum Cleaners Minimising the Time Needed for Complete Dust Removal
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EXAMENSARBETE INOM TECHNOLOGY, GRUNDNIVÅ, 15 HP STOCKHOLM, SVERIGE 2018 Improving robotic vacuum cleaners Minimising the time needed for complete dust removal ANDREAS GYLLING EMIL ELMARSSON KTH SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Improving robotic vacuum cleaners Minimising the time needed for complete dust removal Andreas Gylling Emil Elmarsson Handledare: Jana Tumova Examinator: Orjan¨ Ekeberg KTH School of Electrical Engineering and Computer Science June 1, 2018 ii Abstract The purpose of this study was to examine the cleaning efficiency of an au- tonomous vacuum cleaner robot; namely, reducing the cleaning time needed in an empty room. To do this we explored how the path planning could be improved upon given access to a dust map that would allow for more sophis- ticated algorithms depending on the state of the room. The approach we employed in order to compare different preprogrammed path patterns and our own greedy heuristic was to create a simulation environment in Unity3d. In this environment we could create a two dimensional plane to represent the length and width of a room with the size of our choosing. This plane was then subdivided into squared cells that would discretise the environment, which represented the dust map of the room. The tests were conducted in rooms with different dimensions in order to examine how different strategies' efficiency developed in relation to each other. Employing an algorithm like our greedy heuristic after an initial zigzag sweep resulted in a significant im- provement in comparison to a robot that is restricted to template patterns only. Future work could involve finding the optimised solution for our heuris- tic in order to make full use of the dust map and thereby achieve minimal cleaning time for the robot. iii F¨orb¨attrarobotdammsugare Minimering av tiden som beh¨ovs f¨orfullst¨andigrenhet Sammanfattning Syftet med denna studie var att unders¨oka en sj¨alvstyrande robotdammsug- ares st¨adningseffektivitet och att minimera tiden som kr¨avsf¨oratt st¨ada ett tomt rum. Vi unders¨oktehur roboten kunde planera sina v¨agval b¨attre genom att f˚atillg˚angtill en dammkarta, det vill s¨agaett rutn¨at som h˚aller reda p˚adammf¨ordelningeni ett rum. Denna dammkarta m¨ojligg¨orf¨ormer sofistikerade algoritmer som beror p˚astatusen i rummet. Vi j¨amf¨ordeolika r¨orelsem¨onstersom sicksack, spiralen och v¨aggkramarenmed en egendefinierad heuristik. Ett enkelt ¨oppet rum simulerades i Unity3d, d¨arvi testade de olika algoritmerna. Rummet bestod av ett tv˚adimensionelltplan som representer- ade l¨angdenoch bredden av rummet som gick att justera till v˚arttycke. Detta plan delades sedan upp i ett rutn¨atsom representerar v˚ardiskretiser- ade dammkarta f¨orrummet. Testerna utf¨ordesi rum av olika dimensioner f¨oratt se hur de olika st¨adstrategiernaseffektivitet utvecklades i relation till varandra. Att anv¨andasig av en algoritm s˚asom v˚arheuristik efter ett f¨orsta svep med ett sicksackm¨onster resulterade i en signifikant f¨orb¨attringj¨amf¨ort med en robot som ¨arbunden till f¨ordefinierader¨orelsem¨onster. Framtida studier skulle kunna f¨ors¨oka hitta den optimala l¨osningentill v˚arheuris- tik, s˚aatt dammkartan kan utnyttjas till fullo, och d¨armeduppn˚aminimal st¨adningstidf¨orroboten. iv Keywords autonomous robotic vacuum cleaner, dust map, path planing, coverage v Contents 1 Introduction 1 1.1 Purpose . .2 1.2 Problem statement . .2 1.3 Assumptions . .3 1.4 Terms and definitions . .5 2 Background 6 2.1 Cell decomposition . .6 2.2 Path planning . .7 2.3 Template patterns . .8 2.4 PPCR algorithms . .8 2.5 Related work . .9 2.5.1 Algorithm 1: The pattern method . 10 2.5.2 Algorithm 3: The genetic method . 11 2.5.3 TSP-based: Hess' algorithm . 12 2.5.4 Algorithm 2: The greedy method . 13 3 Methods 15 3.1 Implemented path planning algorithms . 15 3.1.1 Zigzag . 15 3.1.2 Spiral . 16 3.1.3 Wall-to-wall . 17 3.1.4 Greedy PPCR algorithm . 18 3.1.5 Greedy heuristic . 18 3.2 Cleaning strategies . 19 3.2.1 Strategy 1: pattern-based strategy . 19 3.2.2 Strategy 2: Proposed greedy heuristic after an initial sweep . 20 vi 3.2.3 Strategy 3: Greedy strategy . 20 3.2.4 Zigzag's direction . 21 3.3 Building the test system . 21 3.4 Benchmarks . 22 4 Results 25 4.1 Obstacle free room with more dust along the walls . 26 4.1.1 Time . 26 4.1.2 Turns . 28 4.1.3 Distance . 29 4.1.4 Discussion . 31 4.2 Obstacle free room with more dust at the centre of the room . 31 4.2.1 Time . 32 4.2.2 Turns . 33 4.2.3 Distance . 35 4.2.4 Discussion . 36 4.3 Obstacle free room with uniform dust distribution . 37 4.3.1 Time . 37 4.3.2 Turns . 39 4.3.3 Distance . 40 4.3.4 Discussion . 42 4.4 Consequence of zigzag choosing different ways . 42 4.4.1 Turns . 42 4.4.2 Time . 43 4.4.3 Discussion . 44 4.5 Room with obstacles . 45 5 Discussion 48 5.1 The obstacle free room . 48 5.2 The room with obstacles . 49 5.3 Limitations and possible further improvements of the heuristic 50 vii 5.4 Reflection on the hypotheses . 51 5.5 General matters . 53 6 Conclusion 54 viii 1 Introduction Path planning of autonomous robots has been a subject of interest since the beginning of robotics. The goal is to create an algorithm for the robot in order to manoeuvre within an environment and perform some tasks without human assistance. The task could range from navigating through a maze and finding the exit, steering a car on a highway, mowing the lawn, to vacuum cleaning a living room. We will focus on the latter; namely autonomous vacuum cleaner robots. More specifically, we will aim for the robot to try to attain a completely clean room in reasonable time. Modern robotic vacuum cleaners use multiple types of optical sensors, ul- trasound, proximity sensors, lasers, magnets and/or cameras to navigate in their environment. These sensors are then used in combination with different predetermined path planning algorithms such as wall-to-wall, zigzag, random walk and spiral patterns. These could be executed separately or combined in order to ensure that maximum dust removal is achieved (Hasan et al. 2014). To monitor and assess the cleanliness of the environment the robots could use a so-called dust map which tracks the dust level in the entire room (Lee & Banerjee 2015). Lee & Banerjee state that there are three different strategies that the robotic vacuum cleaner could employ for its path planning. These are the follow- ing: 1. Using a dust map which divides the room into cells and measures the amount of dust in each one. Algorithms that use this map can then generate efficient paths depending on which cell is still dirty. 2. A template based strategy which uses predefined path patterns (wall- to-wall, zigzag, etc.) 3. AI based methods. These methods have several learning techniques for 1 generating adaptive paths. In order to do this continuously information needs to be gathered from the environment. The focus of our study will be on combining strategy 1 and 2 since we have chosen to adopt a static environment, i.e. an environment in which there are no moving people or furniture. In such environments there are no need for adaptive paths. 1.1 Purpose The purpose of this case study is to examine the cleaning efficiency of an autonomous vacuum cleaner robot. Given access to a dust map, how would the robot determine an effective cleaning path? Could the robot reduce the cleaning time and consequently decrease the power consumption by combin- ing several preset patterns? Are there certain environments where such a combination would be particularly advantageous? How could we improve upon modern robotic vacuum cleaners in order to reduce the time needed to clean a room? 1.2 Problem statement Our main focus will be on determining which path planning algorithms pro- duce the most efficient result in an open room without obstacles. However, we will also analyse how the efficiency is affected when obstacles are present in the room. We wish to find which advantages and disadvantages there are between different preprogrammed algorithms, assuming the environment is given and set. For simplicity, we assume that the dust map is given. It can be obtained by performing a first full-coverage sweep while simultaneously using sensors to detect the amount of dust remaining under the robot. From now 2 on, our attention will be restricted to planning after the first sweep in order to minimise the cleaning time. We aim to investigate the following: • Given that the environment is known and static and with the help of a dust map; how should the robot move in the room's current state in order to minimise the time needed for complete dust removal? • How does the efficiency of the robot differ between different environ- ments; namely, an empty room and one filled with obstacles? In particular, we are interested in the following hypotheses which are based on intuition: • H1: As more dust is generally accumulated along the walls and corners of a room, following the walls of the room after a first sweep will reduce the cleaning time as the robot only has to clean dirty cells. • H2: Moving towards the nearest uncleaned cell after the initial full sweep will reduce the required cleaning time compared to predefined patterns. • H3: Employing a dust map algorithm will outperform template pat- terns no matter the state of the room.