Household RoboticsAutonomous Devices for Vacuuming and Lawn Mowing

HAYDAR SAHIN andand LEVENT GÜVENÇ

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Service robots are programmable automated or semi-automated mechanical devices designed to perform aspecific service rather than a manufacturing function

[1]. Robots were initially used in the automation sector tohandle repetitive and simple tasks reliably, with the objec-tive of cost reduction per product. Along with the increasedspeed of embedded microcontrollers, the service roboticsector has started to grow [2]. Figure 1 provides a task-based classification of robots in which service robots aredivided into several subcategories. Domestic robots aredesigned to assist humans with tasks such as vacuumcleaning, lawn mowing, and window cleaning. The 2005-2008 sales projection for all types of domestic robots is 4.5million units, with an estimated value of US$3 billion [3].The focus of this article is on domestic robots for vacuum-ing and lawn mowing.

AUTONOMOUS MOBILE ROBOTICS TECHNOLOGYDomestic robots for vacuuming and lawn mowing are mobileunits that use autonomous mobile robotics technology. Out-door robots, such as those for lawn mowing, are designed toavoid rollover and collision incidents [4]. Indoor robots, suchas those for vacuuming, have less demanding environmentalconditions but still face obstacles. Obstacle-avoidance tech-

nology is thus applicable to both cleaning and lawn-mowingrobots. [5]. Collision avoidance is discussed in [6].

Real-time control of autonomous vehicles is designedusing an embedded systems architecture. A field bus isused for communication between sensors, controllers, andnavigation system modules [7].

SENSORSAlthough sensor technology is continually improving, the costof sensors is often too high for use in mass-produced servicerobots. Sensors that have been implemented in operationalsystems include global positioning system (GPS) receivers,laser range finders, and cameras for navigation; ultrasonicsensors, infrared sensors, cameras, and tactile sensors forobstacle avoidance and to locate charging stations; and cliff-detection sensors to avoid falling. All of these sensors can beused in domestic robots as well. Multiple sensors for the samesignal type can be used for safety and redundancy.

SENSOR FUSION, NAVIGATION, AND ARCHITECTURESensor redundancy and sensor fusion contribute toimproved navigation and increased reliability. Sensorfusion for position estimation based on the Kalman filter isdiscussed in [8].

FIGURE 1 Robot types. Domestic robots under the service-robot category assist humans in performing everyday chores. This task-basedclassification of robots shows the location of domestic robots in the robot family tree.

Robots

Industrial Robots Service Robots

DomesticRobots

EntertainmentRobots

Robots forHazardous

Environments

ConstructionRobots

Medical PurposeRobots

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Navigation algorithms use localization, mapping, andmotion planning for obstacle avoidance. Simultaneouslocalization and mapping allow mobile robots to localizethemselves in static surroundings and to adapt to dynamicsurroundings [7].

Distributed architecture control systems using a com-mon communication network such as CAN are modularand inexpensive for integrating the controller, sensor, andactuator nodes [9]. Wireless router protocols such as802.11b and ZigBee are also used for mobile robots.

MULTIPLE MOBILE ROBOTSSome tasks, such as mowing a large lawn, may require ateam of robots. Therefore, technology for controlling multi-ple mobile robots is needed [7]–[10].

HUMAN INTERACTION WITH AUTONOMOUSDOMESTIC MOBILE ROBOTSCustomer evaluations for autonomous domestic robotsreveal that autonomy is perceived differently by differentpeople. For example, people with a desire for keepingeverything under control are reluctant to hand over thecleaning task to a robot. Highly autonomous products areperceived as more risky and complex as compared to lessautonomous products [11]. On the other hand, some peopletend to prefer multipurpose mobile robots that can be usedto guard the house while mowing the lawn or vacuuming.This additional functionality adds more value to the prod-uct and affects consumer evaluations positively [12].

When the level of autonomy in an autonomous mobilerobot increases, both its malfunction rate and complexityincrease as well, making it more difficult to operate andmaintain. Therefore, graphical indicators are needed toshow the task that the robot is operating, while the usermust be able to control the actions of the robot.

AUTONOMOUS LAWN-MOWING ROBOTSPosition estimation using GPS and dead reckoning areused by autonomous lawn mowers. Techniques for deadreckoning include localization using laser scanners, sonars,cameras, and differential GPS [13].

Region filling is a path-planning strategy forautonomous lawn mowers that covers the area to bemowed. A region-filling algorithm is considered in [14]based on neural networks. For this algorithm the obstaclesand wall boundaries do not have to be known in advance.Furthermore, the working space does not need to be divid-ed into subregions, and the vehicle does not have to mem-orize the complete map of the region.

Commercially available autonomous lawn-mowingrobots include the Husqvarna Automower, shown inFigure 2, the Friendly Robotics Robomower, shown inFigure 3, and the Zucchetti Lawnbott. The Lawnbott, whichcan mow up to about half an acre with slopes of up to 27◦,has a perimeter wire and pegs that can be attached to the

ground. A sinusoidal signal goes into the wire, which com-mands the mower to stay in the region of the perimeterwire. With an extra channel for a transmitter, two Lawn-botts can operate in the same region without colliding. Thesinusoidal transmitter for the Lawnbott has the same func-tionality as the perimeter switch of the Robomower.

The Lawnbott stops, turns, and maneuvers until it col-lides with an obstacle. This device automatically rechargeswhen its lightweight, lithium-ion battery reaches a speci-fied level. The Lawnbott has two motors, one on each rearwheel. Its rain sensor and wet-grass detection system trig-ger the mower to return to the charging station. The fre-quency of the cutting can be calculated by the mowerbased on the size of the region and the resistance of thegrass. A maximum of three different regions can be pro-grammed for consecutive mowing [15], [16].

The most significant element of the Robomower is itssafety function, which stops when it moves outside a pre-defined perimeter. The maximum mowable area is 21,500ft2 with a slope of up to 15◦. If the mower, which moves ata slow pace, bumps into an obstacle it stops and turns in adifferent direction. The Robomower mows in a V-shaped

FIGURE 2 Automower from Husqvarna. Hobbyists use autonomouslawn mowers like the Automower as an inexpensive platform forbuilding mobile robots.

FIGURE 3 Robomower from FriendlyRobotics. The Robomoweruses V-shaped patterns for mowing. When the Robomower detectsan obstacle, it stops and turns.

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pattern, continually turning and mowing uncut grass. Atthe end of the third mowing pass the Robomower leaveslittle uncut grass [17].

AUTONOMOUS VACUUM-CLEANING ROBOTSAutonomous vacuum cleaners are application-specificmobile robots used for cleaning purposes. The shape, sensorsystems, and intelligent controls built into these devicesneed to be designed and integrated as a whole. Their shapeis designed to optimize both the quantity of the sensors andthe control unit. The sensor system provides informationabout the environment of the autonomous vacuum cleaner.Reliable and simple algorithms can be implemented for spe-cific spatial domains and sensors [18].

Autonomous vacuum cleaners can be classified as hav-ing simple shapes, round shapes, and shapes with arms.Although a round-shaped autonomous vacuum cleanercan navigate around various shaped obstacles, it cannot

access corners. The addition of an arm on a mobile robotcan reduce the complexity of navigation around obstaclesand increase accessibility into corners and narrow spaces.The sensors need to be adapted to the cleaning tasks andthe shape of the autonomous vacuum cleaner. The speedof the autonomous vacuum cleaner must match the rate ofaccumulation of dust. To facilitate this behavior, theautonomous vacuum cleaner must be integrated with asensor that measures the quantity of dust collected [18].

Navigation based on sensory information is requiredfor autonomous cleaning robots in unknown environ-ments. Sensor-based navigation consists of a lower layerfor hardware, a sensory behavior layer of motion tem-plates, and an upper layer for task-based navigation. Thelower layer encompasses perception, self-localization,actuator motors, charging dock, dust collecting, and powersupply. The sensory behavior layer consists of pointwiseturning, line following, wall following, side shifting, andobstacle rounding templates. Task-based navigation can beused for learning the surroundings, cleaning the area, andnavigating back home or to the charger [19].

Robot vacuums have some handicaps. For instance, cor-ners cannot be cleaned by autonomous vacuum cleanerswith the exception of the Hitachi robot. Maintenancedesign remains challenging, and device cleaning must bedone using powerful manual vacuum cleaners. While thedesign of a robot cleaner that is completely autonomous isunder consideration, cost is an obstacle [20].

Commercially available autonomous vacuum cleaningrobots include the RoboCleaner from Karcher, the Roombafrom iRobot, and the Tribolite from Electrolux. Artificialintelligence techniques are used in the Tribolite autonomousvacuum cleaner as shown in figures 4 and 5 [21]. Artificialintelligence planning is based on a model of the environ-ment constructed by circumnavigating the walls of a room.To traverse the floor area, a plan is created, actions areestablished to achieve the plan, and obstacles and suddenchanges in the path are handled by replanning.

Both RoboCleaner and Electrolux Trilobite have theability to return to their charging station independentlyand resume vacuuming [17]. These vacuum cleaners stopif they get caught under a chair or other obstacle. Unlikethe Trilobite, the RoboCleaner also has the ability to emptyitself when the dust collector is full.

While the Trilobite cleans, it establishes a map of theroom. The navigation for this task is accomplished usingultrasound sensors and magnetic stripes to determine thepresence of stairs or doorways. RoboCleaner can rechargeitself, empty its dust, and stroll without supervision fromone section of the house to another. Roomba and Robo-Cleaner can both sense walls and barriers using tactile sen-sors while wandering around the house. The RoboCleaneris small enough to reach into many places without hittingstairs or getting trapped. By keeping track of dust acquisi-tion, the system focuses on historically dusty locations.

FIGURE 4 Trilobite from Electrolux. This device returns to the charg-ing station, charges itself, and continues vacuuming.

FIGURE 5 Trilobite from Electrolux in action. The Trilobite establish-es a map of the room as it vacuums. Magnetic strips are needed tomark stairs and doors.

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Karcher states that its product is self-sufficient and canoperate without supervision.

Hitachi is working on a multipurpose product thatalso guards, in addition to cleaning or lawn mowing.Their planned robot cleaner can work manually orautonomously and can be controlled by a cell phone. TheHitachi robot cleaner can recharge by itself when neces-sary and dump its dust into a container when full. Likethe Trilobite vacuum, the Hitachi robot cleaner establish-es a map of the house as it moves. The device keeps trackof the cleaned areas while remembering the shapes of theobstacles. It is equipped with a hose for cleaning cornersas well as sensors for preventing dangerous situationssuch as entrapment in small places.

HOBBY AND RESEARCH APPLICATIONSHobbyists and researchers often use household robots as abase for building mobile robots. Until recently, mobilerobotic platforms were available only to researchers work-ing in that area. These mobile robots were tall and widestructures since they needed to house a large array ofsensors and the control computer. Their price was also high,and thus only researchers who had access to such deviceswere able to verify their results experimentally. Such mobilerobots were definitely out of reach for the hobbyist.

Due to the availability and decreasing cost of embed-ded microcontrollers and sensors, cheaper and smallermobile robots have become available for both hobby andresearch applications. Both hobbyists and researchers,however, have realized that using the currently availabledomestic robots as a base for building a mobile robot is aless expensive and more effective solution. Vacuum-clean-ing robots are used to build mobile robots for indoor appli-cations, while lawn-mowing robots are used to buildmobile robots for outdoor applications.

Since the Roomba iRobot costs about US$200, it hasreceived much attention from mobile robot hobbyistswho have replaced its microprocessor with their own,renaming it the Zoomba [22]. Hobbyists mount a laptopwith wireless access and a USB camera to obtain aremotely operated vision system. The modified Roombacan be controlled from a desktop PC through wirelessnetworking. From a researcher’s perspective, the Zoombaprovides an inexpensive mobile robot. An interestinghobby application is Sumo wrestling of two modifiedRoombas with their bump sensors disabled. The compa-ny iRobot that manufactures the Roomba has also startedselling software and an interface for reprogramming.

CONCLUSIONSAs prices drop and performance improves, it is expectedthat robots will find increasing use in our homes. Thesenewly available domestic mobile robots, however, willhave more of an impact on the future of research andhobby applications of mobile robots. The low cost of these

products makes them an excellent choice as a base forbuilding a mobile robot. At present, users create their owninterfaces to these products to change the controllers andadd sensors. The producers of these domestic mobilerobots are starting to realize that the hobbyists andresearchers interested in their products form a large mar-ket and are beginning to provide the necessary interfaceproducts themselves. The future of the domestic mobilerobotics industry is promising. We should get ready toplay with the software of our robotic vacuum cleaner andlawn mower for more personalized cleaning and mowingin the near future. The future is also promising for thosewho wish to modify a vacuum cleaner to bring tools,serve drinks, or act like a guard dog.

REFERENCES[1] M. Schofield, “Neither master nor slave...,” in Proc. IEEE Symp. EmergingTechnologies and Factory Automation, ETFA, 1999, vol. 2, pp. 1427–1434.[2] I.F. of Robotics, “Service robots,” 2005 [Online]. Available:http://www.ifr.org/pictureGallery/servRobAppl.htm[3] I.F. of Robotics, “The world market of service robots,” 2005. [Online].Available: http://www.ifr.org/statistics/keyData2005.htm[4] A. Yahja, S. Singh, and A. Stentz, “An efficient on-line path planner for out-door mobile robots,” Robot. Autonomous Syst., vol. 32, pp. 129–143, Aug. 2000.[5] H. Moravec, “Seegrid corporation,” 2005. [Online]. Available:http://www.frc.ri.cmu. edu/hpm/seegrid.html[6] B. Graf, M. Hans, and R.D. Schraft, “Robot assistants,” IEEE Robot.Automat. Mag., vol. 11, pp. 67–77, June 2004.[7] B.L. Brumitt and A. Stentz, “GRAMMPS: A generalized mission plannerfor multiple mobile robots in unstructured environments,” in Proc. IEEEConf. Robotics and Automation, 1998, vol. 2, pp. 1564–1571.[8] S.J. Julier and H.F. Durrant-Whyte, “On the role of process models inautonomous land vehicle navigation systems,” IEEE Trans. Robot. Automat.,vol. 19, pp. 1–14, Feb. 2003.[9] U. Nunes, J.A. Fonseca, L. Almeida, R. Araujo, and R. Maia, “Using dis-tributed systems in real-time control of autonomous vehicles,” Robotica, vol.21, pp. 271–281, May/June 2003.[10] H.C.-H. Hsu and A. Liu, “Multiagent-based multi-team formation con-trol for mobile robots,” J. Intell. Robot. Syst.: Theory Applicat., vol. 42, pp.337–360, April 2005.[11] Fujitsu, “Fujitsu develops mobile phone-controlled robot for the home,”2002. [Online] Available: http://pr.fujitsu.com/en/news/2002/10/7.html#2[12] S.A. Rijsdijk and E.J. Hultink, “Honey, have you seen our hamster?”Consumer evaluations of autonomous domestic products,” J. Product Innova-tion Manag., vol. 20, pp. 204–216, May 2003.[13] H. Huang, “Bearing-only slam,” 2006 [Online]. Available:http://www.fit.qut.edu.au/ research/papers/huang.jsp[14] C. Luo, S.X. Yang, and M. Meng, “Entire region filling in indoor envi-ronments using neural networks,” in Proc. World Congress Intelligent Controland Automation (WCICA), 2002, vol. 3, pp. 2039–2044.[15] “The lawnbott evolution revolution,” 2005 [Online]. Available:http://www.bamabots. com/Kerry/112805ambrogio.htm[16] “Robomower review,” 2005 [Online]. Available: http://www.bam-abots.com/rl1000review. htm[17] “The robot store is an authorized distributor and service center for:Karcher rc3000 robocleaner: Friendly robotics products: Electrolux el520atrilobite: Robotic vacuum,” 2003 [Online]. Available: http://www.therobot-store.com/s.nl/sc.9/category.-109/.f[18] I. Ulrich, F. Mondada, and J.-D. Nicoud, “Autonomous vacuum clean-er,” Robot. Autonomous Syst., vol. 19, pp. 233–245, Mar 1997.[19] Y. Liu, S. Zhu, B. Jin, S. Feng, and H. Gong, “Sensory navigation ofautonomous cleaning robots,” in Proc. World Congr. Intelligent Control andAutomation (WCICA), 2004, pp. 4793–4796.[20] L. Kaehney, “Robot vacs are in the house,” 2003 [Online]. Available:http://www.wired. com/news/technology/0,1282,59237,00.html

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particular, the U.S. Particle AcceleratorSchool (USPAS) is a national graduateschool that provides educational pro-grams in the field of beams and associ-ated accelerator technologies nototherwise available to scientists andengineers. Established in 1981, theUSPAS is governed and funded by aconsortium of laboratories under boththe Office of Science (High EnergyPhysics) and the National NuclearSecurity Agency of the DOE, as wellas the NSF. The Carolus Magnus Sum-mer School on Plasma and FusionEnergy Physics and the Culham Plas-ma Physics Summer School, bothunder the auspice of EURATOM, areexamples of this approach in Europe.

Collaboration Between the Controland Fusion CommunitiesIt is critical to establish funding mecha-nisms to allow the two communities towork together. Current joint NSF/DOE programs in the area of plasmaphysics should become open to thistype of research. New programsshould be created as the result of ajoint effort between NSF and DOE.

For the past ten years, physicistsworking on control problems in fusionhave held an annual physics workshopon Active Control of MHD Stability afterthe American Physical Society Meeting ofthe Division of Plasma Physics. Likewise,periodic workshops can provide a mech-anism for enhancing collaboration

between the fusion and control commu-nities. To facilitate such workshops, a pro-posal has been made to hold a control-oriented workshop before or after majorcontrol conferences such as the AmericanControl Conference or the IEEE Confer-ence on Decision and Control.

ACKNOWLEDGMENTSThe workshop organizers EugenioSchuster, Michael Walker, andMiroslav Krstic thank the NSF (Dr.Mary Ann Horn), the U.S. DOE Officeof Fusion Energy Sciences, and Gener-al Atomics for supporting this event.

Eugenio SchusterMichael Walker

[21] A.A. Hopgood, “Artificial intelligence: Hypeor reality?,” Computer, vol. 36, pp. 24–28, May 2003.[22] “Roomba community,” 2006 [Online]. Avail-able: http://www.roombacommunity.com

AUTHOR INFORMATIONHaydar Sahin received the B.S. degreein mechanical engineering from Istan-bul Technical University in 1992 andthe M.S. degree in mechanical engi-neering from Rochester Institute ofTechnology in 1997. He is currently aPh.D. student in the Mechanical Engi-

neering Department at Bogazici Uni-versity. His research interests are incontrol of vehicle chassis systems.

Levent Güvenç received the B.S.degree in mechanical engineering fromBogazici University, Istanbul, in 1985,the M.S. degree in mechanical engi-neering from Clemson University in1988, and the Ph.D. degree in mechani-cal engineering from the Ohio StateUniversity in 1992. Since 1996, he hasbeen working in the mechanical engi-neering department of Istanbul Techni-

cal University, where he is currently aprofessor of mechanical engineeringand director of the MechatronicsResearch Lab and the EU-fundedAutomotive Controls and Mechatron-ics Research Center. He has more than80 technical publications in controls,robotics, and mechatronics and is acoauthor of Robust Control: the Parame-ter Space Approach. His current researchinterests are on automotive controlmechatronics, helicopter stability andcontrol, and applied robust control.

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Hidden Assumption

Given the simple differential equation dx/dt = f (t), Heaviside would writepx(t) = f (t) and then solve for x(t) as

x(t) = 1p

f (t) =∫ t

0f (u)du.

That is, he associated the 1/p operator with the definite integral over theinterval zero to t. This implies, however, that x(0) = 0, which may not alwaysbe the case. In those situations where this is not true, the p and 1/p operatorsare not inverse operators; the result of this has often been the calculation oferroneous results by the unwary.

—From P.J. Nahin, Oliver Heaviside: Sage in Solitude: The Life, Work, and Times of an Electrical Genius of the Victorian Age, IEEE Press, 1988, p. 232.

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