divided into several subcategories. Domestic robots are designed to assist humans with tasks such as vacuum cleaning, lawn mowing, and window cleaning.
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Household Robotics Autonomous Devices for Vacuuming and Lawn Mowing HAYDAR SAHIN ¸ and LEVENT GÜVENÇ
ervice robots are programmable automated or semiautomated mechanical devices designed to perform a specific service rather than a manufacturing function [1]. Robots were initially used in the automation sector to handle repetitive and simple tasks reliably, with the objective of cost reduction per product. Along with the increased speed of embedded microcontrollers, the service robotic sector has started to grow [2]. Figure 1 provides a taskbased classification of robots in which service robots are divided into several subcategories. Domestic robots are designed to assist humans with tasks such as vacuum cleaning, lawn mowing, and window cleaning. The 20052008 sales projection for all types of domestic robots is 4.5 million units, with an estimated value of US$3 billion [3]. The focus of this article is on domestic robots for vacuuming and lawn mowing.
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AUTONOMOUS MOBILE ROBOTICS TECHNOLOGY Domestic robots for vacuuming and lawn mowing are mobile units that use autonomous mobile robotics technology. Outdoor robots, such as those for lawn mowing, are designed to avoid rollover and collision incidents [4]. Indoor robots, such as those for vacuuming, have less demanding environmental conditions but still face obstacles. Obstacle-avoidance tech-
nology is thus applicable to both cleaning and lawn-mowing robots. [5]. Collision avoidance is discussed in [6]. Real-time control of autonomous vehicles is designed using an embedded systems architecture. A field bus is used for communication between sensors, controllers, and navigation system modules [7].
SENSORS Although sensor technology is continually improving, the cost of sensors is often too high for use in mass-produced service robots. Sensors that have been implemented in operational systems include global positioning system (GPS) receivers, laser range finders, and cameras for navigation; ultrasonic sensors, infrared sensors, cameras, and tactile sensors for obstacle avoidance and to locate charging stations; and cliffdetection sensors to avoid falling. All of these sensors can be used in domestic robots as well. Multiple sensors for the same signal type can be used for safety and redundancy.
SENSOR FUSION, NAVIGATION, AND ARCHITECTURE Sensor redundancy and sensor fusion contribute to improved navigation and increased reliability. Sensor fusion for position estimation based on the Kalman filter is discussed in [8].
Robots
Industrial Robots
Service Robots
Domestic Robots
Entertainment Robots
Robots for Hazardous Environments
Construction Robots
Medical Purpose Robots
FIGURE 1 Robot types. Domestic robots under the service-robot category assist humans in performing everyday chores. This task-based classification of robots shows the location of domestic robots in the robot family tree.
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Navigation algorithms use localization, mapping, and motion planning for obstacle avoidance. Simultaneous localization and mapping allow mobile robots to localize themselves in static surroundings and to adapt to dynamic surroundings [7]. Distributed architecture control systems using a common communication network such as CAN are modular and inexpensive for integrating the controller, sensor, and actuator nodes [9]. Wireless router protocols such as 802.11b and ZigBee are also used for mobile robots.
MULTIPLE MOBILE ROBOTS Some tasks, such as mowing a large lawn, may require a team of robots. Therefore, technology for controlling multiple mobile robots is needed [7]–[10].
FIGURE 2 Automower from Husqvarna. Hobbyists use autonomous lawn mowers like the Automower as an inexpensive platform for building mobile robots.
HUMAN INTERACTION WITH AUTONOMOUS DOMESTIC MOBILE ROBOTS Customer evaluations for autonomous domestic robots reveal that autonomy is perceived differently by different people. For example, people with a desire for keeping everything under control are reluctant to hand over the cleaning task to a robot. Highly autonomous products are perceived as more risky and complex as compared to less autonomous products [11]. On the other hand, some people tend to prefer multipurpose mobile robots that can be used to guard the house while mowing the lawn or vacuuming. This additional functionality adds more value to the product and affects consumer evaluations positively [12]. When the level of autonomy in an autonomous mobile robot increases, both its malfunction rate and complexity increase as well, making it more difficult to operate and maintain. Therefore, graphical indicators are needed to show the task that the robot is operating, while the user must be able to control the actions of the robot.
AUTONOMOUS LAWN-MOWING ROBOTS Position estimation using GPS and dead reckoning are used by autonomous lawn mowers. Techniques for dead reckoning include localization using laser scanners, sonars, cameras, and differential GPS [13]. Region filling is a path-planning strategy for autonomous lawn mowers that covers the area to be mowed. A region-filling algorithm is considered in [14] based on neural networks. For this algorithm the obstacles and wall boundaries do not have to be known in advance. Furthermore, the working space does not need to be divided into subregions, and the vehicle does not have to memorize the complete map of the region. Commercially available autonomous lawn-mowing robots include the Husqvarna Automower, shown in Figure 2, the Friendly Robotics Robomower, shown in Figure 3, and the Zucchetti Lawnbott. The Lawnbott, which can 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
FIGURE 3 Robomower from FriendlyRobotics. The Robomower uses V-shaped patterns for mowing. When the Robomower detects an obstacle, it stops and turns.
ground. A sinusoidal signal goes into the wire, which commands the mower to stay in the region of the perimeter wire. With an extra channel for a transmitter, two Lawnbotts can operate in the same region without colliding. The sinusoidal transmitter for the Lawnbott has the same functionality as the perimeter switch of the Robomower. The Lawnbott stops, turns, and maneuvers until it collides with an obstacle. This device automatically recharges when its lightweight, lithium-ion battery reaches a specified level. The Lawnbott has two motors, one on each rear wheel. Its rain sensor and wet-grass detection system trigger the mower to return to the charging station. The frequency of the cutting can be calculated by the mower based on the size of the region and the resistance of the grass. A maximum of three different regions can be programmed for consecutive mowing [15], [16]. The most significant element of the Robomower is its safety function, which stops when it moves outside a predefined perimeter. The maximum mowable area is 21,500 ft2 with a slope of up to 15◦ . If the mower, which moves at a slow pace, bumps into an obstacle it stops and turns in a different direction. The Robomower mows in a V-shaped
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pattern, continually turning and mowing uncut grass. At the end of the third mowing pass the Robomower leaves little uncut grass [17].
AUTONOMOUS VACUUM-CLEANING ROBOTS Autonomous vacuum cleaners are application-specific mobile robots used for cleaning purposes. The shape, sensor systems, and intelligent controls built into these devices need to be designed and integrated as a whole. Their shape is designed to optimize both the quantity of the sensors and the control unit. The sensor system provides information about the environment of the autonomous vacuum cleaner. Reliable and simple algorithms can be implemented for specific spatial domains and sensors [18]. Autonomous vacuum cleaners can be classified as having simple shapes, round shapes, and shapes with arms. Although a round-shaped autonomous vacuum cleaner can navigate around various shaped obstacles, it cannot
FIGURE 4 Trilobite from Electrolux. This device returns to the charging station, charges itself, and continues vacuuming.
FIGURE 5 Trilobite from Electrolux in action. The Trilobite establishes a map of the room as it vacuums. Magnetic strips are needed to mark stairs and doors.
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access corners. The addition of an arm on a mobile robot can reduce the complexity of navigation around obstacles and increase accessibility into corners and narrow spaces. The sensors need to be adapted to the cleaning tasks and the shape of the autonomous vacuum cleaner. The speed of the autonomous vacuum cleaner must match the rate of accumulation of dust. To facilitate this behavior, the autonomous vacuum cleaner must be integrated with a sensor that measures the quantity of dust collected [18]. Navigation based on sensory information is required for autonomous cleaning robots in unknown environments. Sensor-based navigation consists of a lower layer for hardware, a sensory behavior layer of motion templates, and an upper layer for task-based navigation. The lower layer encompasses perception, self-localization, actuator motors, charging dock, dust collecting, and power supply. The sensory behavior layer consists of pointwise turning, line following, wall following, side shifting, and obstacle rounding templates. Task-based navigation can be used for learning the surroundings, cleaning the area, and navigating back home or to the charger [19]. Robot vacuums have some handicaps. For instance, corners cannot be cleaned by autonomous vacuum cleaners with the exception of the Hitachi robot. Maintenance design remains challenging, and device cleaning must be done using powerful manual vacuum cleaners. While the design of a robot cleaner that is completely autonomous is under consideration, cost is an obstacle [20]. Commercially available autonomous vacuum cleaning robots include the RoboCleaner from Karcher, the Roomba from iRobot, and the Tribolite from Electrolux. Artificial intelligence techniques are used in the Tribolite autonomous vacuum cleaner as shown in figures 4 and 5 [21]. Artificial intelligence planning is based on a model of the environment constructed by circumnavigating the walls of a room. To traverse the floor area, a plan is created, actions are established to achieve the plan, and obstacles and sudden changes in the path are handled by replanning. Both RoboCleaner and Electrolux Trilobite have the ability to return to their charging station independently and resume vacuuming [17]. These vacuum cleaners stop if they get caught under a chair or other obstacle. Unlike the Trilobite, the RoboCleaner also has the ability to empty itself when the dust collector is full. While the Trilobite cleans, it establishes a map of the room. The navigation for this task is accomplished using ultrasound sensors and magnetic stripes to determine the presence of stairs or doorways. RoboCleaner can recharge itself, empty its dust, and stroll without supervision from one section of the house to another. Roomba and RoboCleaner can both sense walls and barriers using tactile sensors while wandering around the house. The RoboCleaner is small enough to reach into many places without hitting stairs or getting trapped. By keeping track of dust acquisition, the system focuses on historically dusty locations.
Karcher states that its product is self-sufficient and can operate without supervision. Hitachi is working on a multipurpose product that also guards, in addition to cleaning or lawn mowing. Their planned robot cleaner can work manually or autonomously and can be controlled by a cell phone. The Hitachi robot cleaner can recharge by itself when necessary and dump its dust into a container when full. Like the Trilobite vacuum, the Hitachi robot cleaner establishes a map of the house as it moves. The device keeps track of the cleaned areas while remembering the shapes of the obstacles. It is equipped with a hose for cleaning corners as well as sensors for preventing dangerous situations such as entrapment in small places.
HOBBY AND RESEARCH APPLICATIONS Hobbyists and researchers often use household robots as a base for building mobile robots. Until recently, mobile robotic platforms were available only to researchers working in that area. These mobile robots were tall and wide structures since they needed to house a large array of sensors and the control computer. Their price was also high, and thus only researchers who had access to such devices were able to verify their results experimentally. Such mobile robots were definitely out of reach for the hobbyist. Due to the availability and decreasing cost of embedded microcontrollers and sensors, cheaper and smaller mobile robots have become available for both hobby and research applications. Both hobbyists and researchers, however, have realized that using the currently available domestic robots as a base for building a mobile robot is a less expensive and more effective solution. Vacuum-cleaning robots are used to build mobile robots for indoor applications, while lawn-mowing robots are used to build mobile robots for outdoor applications. Since the Roomba iRobot costs about US$200, it has received much attention from mobile robot hobbyists who have replaced its microprocessor with their own, renaming it the Zoomba [22]. Hobbyists mount a laptop with wireless access and a USB camera to obtain a remotely operated vision system. The modified Roomba can be controlled from a desktop PC through wireless networking. From a researcher’s perspective, the Zoomba provides an inexpensive mobile robot. An interesting hobby application is Sumo wrestling of two modified Roombas with their bump sensors disabled. The company iRobot that manufactures the Roomba has also started selling software and an interface for reprogramming.
CONCLUSIONS As prices drop and performance improves, it is expected that robots will find increasing use in our homes. These newly available domestic mobile robots, however, will have more of an impact on the future of research and hobby applications of mobile robots. The low cost of these
products makes them an excellent choice as a base for building a mobile robot. At present, users create their own interfaces to these products to change the controllers and add sensors. The producers of these domestic mobile robots are starting to realize that the hobbyists and researchers interested in their products form a large market and are beginning to provide the necessary interface products themselves. The future of the domestic mobile robotics industry is promising. We should get ready to play with the software of our robotic vacuum cleaner and lawn mower for more personalized cleaning and mowing in the near future. The future is also promising for those who 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. Emerging Technologies 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 outdoor 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 planner for multiple mobile robots in unstructured environments,” in Proc. IEEE Conf. Robotics and Automation, 1998, vol. 2, pp. 1564–1571. [8] S.J. Julier and H.F. Durrant-Whyte, “On the role of process models in autonomous 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 distributed 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 control 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 Innovation 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 environments using neural networks,” in Proc. World Congress Intelligent Control and 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.bamabots.com/rl1000review. htm [17] “The robot store is an authorized distributor and service center for: Karcher rc3000 robocleaner: Friendly robotics products: Electrolux el520a trilobite: Robotic vacuum,” 2003 [Online]. Available: http://www.therobotstore.com/s.nl/sc.9/category.-109/.f [18] I. Ulrich, F. Mondada, and J.-D. Nicoud, “Autonomous vacuum cleaner,” Robot. Autonomous Syst., vol. 19, pp. 233–245, Mar 1997. [19] Y. Liu, S. Zhu, B. Jin, S. Feng, and H. Gong, “Sensory navigation of autonomous cleaning robots,” in Proc. World Congr. Intelligent Control and Automation (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 Accelerator School (USPAS) is a national graduate school that provides educational programs in the field of beams and associated accelerator technologies not otherwise available to scientists and engineers. Established in 1981, the USPAS is governed and funded by a consortium of laboratories under both the Office of Science (High Energy Physics) and the National Nuclear Security Agency of the DOE, as well as the NSF. The Carolus Magnus Summer School on Plasma and Fusion Energy Physics and the Culham Plasma Physics Summer School, both under the auspice of EURATOM, are examples of this approach in Europe.
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[21] A.A. Hopgood, “Artificial intelligence: Hype or reality?,” Computer, vol. 36, pp. 24–28, May 2003. [22] “Roomba community,” 2006 [Online]. Available: http://www.roombacommunity.com
AUTHOR INFORMATION Haydar S ¸ ahin received the B.S. degree in mechanical engineering from Istanbul Technical University in 1992 and the M.S. degree in mechanical engineering from Rochester Institute of Technology in 1997. He is currently a Ph.D. student in the Mechanical Engi-
Collaboration Between the Control and Fusion Communities It is critical to establish funding mechanisms to allow the two communities to work together. Current joint NSF/ DOE programs in the area of plasma physics should become open to this type of research. New programs should be created as the result of a joint effort between NSF and DOE. For the past ten years, physicists working on control problems in fusion have held an annual physics workshop on Active Control of MHD Stability after the American Physical Society Meeting of the Division of Plasma Physics. Likewise, periodic workshops can provide a mechanism for enhancing collaboration
neering Department at Bogazici University. His research interests are in control of vehicle chassis systems. Levent Güvenç received the B.S. degree in mechanical engineering from Bogazici University, Istanbul, in 1985, the M.S. degree in mechanical engineering from Clemson University in 1988, and the Ph.D. degree in mechanical engineering from the Ohio State University in 1992. Since 1996, he has been working in the mechanical engineering department of Istanbul Techni-
iven the simple differential equation dx/dt = f (t), Heaviside would write px(t) = f (t) and then solve for x(t) as
G
1 f (t) = p
0
t
f (u)du.
That is, he associated the 1/p operator with the definite integral over the interval zero to t. This implies, however, that x(0) = 0, which may not always be the case. In those situations where this is not true, the p and 1/p operators are not inverse operators; the result of this has often been the calculation of erroneous 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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ACKNOWLEDGMENTS The workshop organizers Eugenio Schuster, Michael Walker, and Miroslav Krstic thank the NSF (Dr. Mary Ann Horn), the U.S. DOE Office of Fusion Energy Sciences, and General Atomics for supporting this event. Eugenio Schuster Michael Walker
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Hidden Assumption
x(t) =
between the fusion and control communities. To facilitate such workshops, a proposal has been made to hold a controloriented workshop before or after major control conferences such as the American Control Conference or the IEEE Conference on Decision and Control.
cal University, where he is currently a professor of mechanical engineering and director of the Mechatronics Research Lab and the EU-funded Automotive Controls and Mechatronics Research Center. He has more than 80 technical publications in controls, robotics, and mechatronics and is a coauthor of Robust Control: the Parameter Space Approach. His current research interests are on automotive control mechatronics, helicopter stability and control, and applied robust control.
