Team Roomba. Introduction. Technology

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1 Team Roomba Introduction You may know Roombas as that futuristic robot vacuum that your uncle bought for himself one Christmas just so he could show it off, but have you ever wondered what makes a Roomba tick? Roombas were first introduced in 2002 as autonomous robotic vacuum cleaners sold by a company called irobot. The first model was quite basic, had limited functionality, and used an algorithm combined with mapping technology to clean areas in a home. Since 2002, irobot has released many iterations of the Roomba, the current model being the 980 series, with advanced technology and improved algorithms for detecting obstacles and navigating large areas. Context Given that many algorithms aimed at performing tasks autonomously are being developed by computer scientists (Yershov 1), we hope that by learning about the early implementations of algorithms and mapping technology, we can develop an understanding of how these devices work and how they have evolved since their inception. By contrasting early implementations with current technology, we learn how a symbiosis has formed between computational algorithms and technology and that limitations exist with the development of both. Since autonomous vehicles have become more prominent over the years, we hope to learn more about how the application of computer science and computational algorithms within the context of selfnavigation and mapping techniques can impact other industries in a positive way. Our Perspective As people become more dependent on autonomous technology, this may pose ethical concerns. However, we believe that further advances in these areas outweigh the risks and will enhance consumer products. With these improvements in technology, products will greatly increase the efficiency of our everyday lives. We believe that the irobot s Roomba vacuums are a great representation of how technology and computer science are merging to create more productive products. We plan to learn about the productivity and efficiency displayed in the Roomba and further this assumption by observing the advantages that the latest Roomba vacuums have compared to the older models. This is shown through the various mapping and navigation techniques and the several algorithms that have been programmed into the Roomba. Technology Sensors and Odometry The Roomba 400 series, introduced in September 2004 contained the same hardware as the original Roomba which had one infrared sensor for obstacle detection, three contact sensors behind the front bumper for responding to collisions, and wheel encoders for odometry measurements. (Haselirad and Neubert 2) The original Roomba primarily used its infrared sensor for avoiding collisions with objects, but it could not determine whether the object was a wall or a stationary object such as a table or a chair. The sensor was located on the right side of the unit, and once activated by coming into close proximity (within

2 two inches) of an object it would only be able to turn left to avoid it. This is a major limitation without the assistance of the contact sensors. The infrared sensor was also used as a way for the Roomba to return to its home base. However, obstacles, bright lights, and other infrared signals typically interfered with this method. The three contact sensors on the Roomba 400 were located on each side of the front bumper, and in the center and these sensors were only activated once the Roomba collided with an object (Haselirad and Neubert 2). Therefore, it was used to determine how to navigate around an object after the Roomba hit it. Depending on which sensor was activated, the Roomba could determine in what direction to turn. This method was useful for when there were not many objects in a room, or if the objects were positioned perpendicular to the walls. In some cases, the Roomba 400 would be able to wedge itself between objects or get stuck in a corner. The wheel odometers were used as a method for determining the Roomba s position relative to its starting point, and so it was a form of a mapping technique. However, there are major limitations of this method leaving too much room for error. When the Roomba bumps an obstacle, it often slips as it pushes forward, causing the robot to rotate. This rotation is not measured by the odometry (Tribelhorn and Dodds 1396). The technology and algorithms used for this early model of the Roomba were relatively simple. However, with continued developments, irobot was able to enhance the technology and algorithms within their Roomba vacuums. The present-day Roombas have numerous onboard sensors for detecting a wide range of objects such as furniture, toys, different types of dirt, and even objects that would cause trouble for it like telephone cords. It also has the ability to detect surface textures so that it can adjust its power accordingly, which is particularly important when traversing carpet or rugs that would require extra power to its wheels. These Roombas are also equipped with a camera and utilize VSLAM (Vision Simultaneous Localization and Mapping) which increases the efficiency of the robot and allows it to cover an entire level of a home with multiple rooms. (Ackerman and Guizzo). Batteries and Power Rechargeable battery packs are another important aspect of the Roomba, especially when traveling for long periods of time. The Roomba 400 carried an Advanced Power System (APS) 3000 mah nickel-metal hydride (NiMH) battery pack which typically took 3-6 hours to charge, or 13 hours if the battery was deeply discharged (irobot). When fully charged, the Roomba was estimated to have on average a 120-minute run time before having to return to its home base to charge. The early Roomba models have high power drain due to their adjustable power and vacuum motors, as such this type of battery outperforms regular alkaline batteries. The NiMH are also ideal because of their trickle-charge, which is a low fixed current charge. This suits the Roomba rather well because it is docked and charging between routes. The 900 series Roombas sported a different type of battery, specifically known as the Roomba 3300 Lithium Ion Battery (irobot). This new battery typically takes 2 hours or more to fully charge and offers up to twice the cleaning cycles of previous models. This is an especially important development for the Roomba. Li-ion and NiMH batteries can hold a similar amount of power, but the lithium-ion cells can be charged and discharged more rapidly, allowing the Roomba 900 to adjust its power quite significantly in an effort to clean a given space. Also, it can stop a cleaning job early so that it can return to its home base to charge before continuing with the cleaning job.

3 Together with the algorithms used to map an area, these later models of the Roomba were much more adept at operating autonomously. With longer battery life, and the ability to pause a cleaning route to charge its battery, the algorithms used in these models are less limited and can include many new options. Scheduling and Mapping Options In both the original and 400 series Roombas, scheduling was done via an optional accessory called the Schedule Remote Control which came with Scheduler Virtual Walls. The Scheduler Remove Control was a separate unit for programming a scheduling time but it was limited to only one cleaning mode. The mapping options were limited to three modes: Figure 1. Courtesy of irobot ( In later models of the Roomba, particularly the 980 series, one significant feature is the ability to program schedules and mapping options, providing the robot with the ability to go on different routes at different times. This is particularly useful in a multi-room home where each room may require a different cleaning schedule or cleaning type. With Wi-Fi capabilities and built-in programming, smartphone users had the ability to download an app for iphone or Android lets you set a cleaning schedule and other preferences without touching your robot (Ackerman and Guizzo). Algorithm Original Roombas The patent for the original Roombas reveals three operational modes: Spot Coverage, Wall Follow, and Room Coverage. Spot Coverage Spot Coverage allows users to clean isolated dirty areas. When the user places the Roomba on the floor near the center of the area and selects the spot coverage mode, the Roomba moves in a way in which the immediate area within a predefined radius contacts the cleaning head or side brush of the Roomba. The preferred method of spot cleaning is to use a control algorithm that causes the Roomba to move in an outward spiral motion. Generally, the spiral movement is generated by increasing the turning radius as a

4 function of time. As time goes on, the larger the spiral becomes. The patent states that keeping the dominant side of the Roomba on the outside of the spiral is preferred. When the Spot Coverage mode is initiated, the Roomba sets the value of r (radius) at its minimum, positive value, which will produce the tightest, counterclockwise turn. The Roomba recalculates the value of r using the equation r=aθ, where a is a constant coefficient (Jones and Mass) and a=d/2π, where d is the distance between two consecutive passes of the spiral (Jones and Mass). The Roomba has multiple spiral distances depending on whether it is performing an initial spiral or a later spiral. Afterwards, the Roomba changes to a different behaviour such as a straight line. As per the patent, if an obstacle is encountered in spiral mode, the Roomba exits spot [coverage] mode upon the first obstacle encountered by a bump sensor (Jones and Mass). Wall Following Wall-following mode allows the user to clean the edges of a room or the edges of objects within a room. In Wall-following mode, the Roomba positions itself with its dominant side towards the wall and at a set distance from the wall using the Wall-following sensor. The Roomba uses the IR transmitter portion of the sensor to sense how far it is away from the wall. The Roomba then moves along the wall. However, the Roomba cannot tell the difference between a wall and another solid obstacle. Per the patent, if the sensor tells the Roomba the object is out of range, then it will veer slightly to the right (Jones and Mass). Minimum and maximum distances are determined depending on room size and configuration, Roomba size, and the number of frequency of objects encountered. More objects encountered would have the Roomba increase the distance it travels in Wall-following, and vice-versa. The minimum and maximum distances ensure the Roomba spends an appropriate amount of time in wall-following behavior, and therefore distribute coverage more evenly. An increase in Wall-following means the Roomba can move in more spaces but is less efficient at cleaning any one space. If the Roomba bumps into something, it goes into Align behavior, which tells the Roomba to turn a minimum angle. After the minimum angle, the Roomba uses its wall sensor to see if the wall detection goes away. If it goes away, the Roomba will stop turning. After the Roomba stops turning, it goes back to Wall-following mode. Room Coverage In Room Coverage mode, the Roomba combines a Bounce behavior with a straight line behavior. The Roomba travels until a bump sensor or sensors are activated. When a sensor or sensors are activated, the Roomba calculates an acceptable range of new directions based on which sensors were activated and the angle at which the object the Roomba has bumped into. The angle is determined by the timing between the sensors. The turn is either clockwise or counterclockwise depending on which way is faster. After turning, the Roomba continues traveling in its new direction. Escape Behaviours The Roomba also has four escape behaviors it can use if it is trapped in areas such as a confined corner or stuck under chairs. In Turn behavior, the Roomba turns in place in a random direction, starting at a high turning velocity and decreasing to a low velocity (Jones and Mass). In Edge behavior, the Roomba will use its bump sensor to follow an edge until the Roomba has turned 60 degrees without a bump or has turned more than 170 degrees after the Edge behavior was initiated (Jones and Mass). In Wheel Drop behavior, the Roomba back drives its wheels briefly then stops them, giving the wheels a small kick in the opposite direction to stop the Roomba from, say, falling down stairs (Jones and Mass).

5 In Slow behavior, if a wheel drop on the cliff detector goes off, the Roomba will slow itself down to 77% of its normal speed for 0.5 meters (Jones and Mass) until it goes back to its normal speed. Present-Day Roombas In the original models of the Roomba, the three operational modes of Spot Coverage, Wall Follow, and Room Coverage were the ways that the robot could navigate through its surroundings and position itself in the room. However, there are many limitations regarding the efficiency of the older Roomba models. With the introduction of the Roomba 980, irobot has greatly enhanced the efficiency of their product mainly through the addition of the VSLAM (Vision Simultaneous Localization and Mapping) algorithm. VSLAM (Vision Simultaneous Localization and Mapping) In their latest Roomba 980 model, irobot has incorporated a new computational algorithm called VSLAM (Vision Simultaneous Localization and Mapping). The VSLAM algorithm aims to fuse image and odometry data for map building and localization (Karlsson et al. 25). With the addition of this algorithm, the Roomba 980 is equipped with new mapping and navigation techniques that enhance the overall performance of the robot. VSLAM allows the Roomba to build a map of the room it currently occupies by utilizing the camera located on top of the vacuum and taking a picture of the room (Ackerman and Guizzo). The image is then processed by the software inside the Roomba and distinctive patterns are found, allowing the Roomba to visualize and track new features of its surroundings (Ackerman and Guizzo). Furthermore, odometry is used alongside the VSLAM algorithm to increase the capabilities of the Roomba. By combining odometry data from sensors on the bottom of the robot and the map created by VSLAM, The Roomba 980 can localize itself in its environment (Ackerman and Guizzo), leading to greater efficiency in cleaning the designated area. The introduction of VSLAM in the Roomba 980 has made cleaning much more efficient compared to irobot s older models. In the older Roombas, the robot would navigate randomly and heavily rely on sensors to change directions (Knight), resulting in an inefficient zigzag pattern of navigation (Ackerman and Guizzo). In contrast, the ability to build a map and localize itself with VSLAM increases the efficiency of the Roomba 980 as it allows the robot to clean in straighter lines (Ackerman and Guizzo). In addition to highly efficient navigation patterns, the mapping algorithm allows the Roomba 980 to memorize its current location. This feature allows the robot to automatically find its way to the charger when it is running on low battery, recharge, then resume cleaning where it left off (Ackerman and Guizzo). The older models of irobot s Roombas are also capable of finding its way back to the charger but without the ability to create a map, they cannot resume cleaning from where it left off (Ackerman and Guizzo). This reveals a significant advantage that the VSLAM algorithm brings to the new Roomba 980 as it is not necessary to reset the robot after it finishes recharging. VSLAM is a crucial component of the Roomba 980 and the features of this algorithm substantially enhance the performance of the robot, making it more efficient than the older Roombas. Pitfalls While the original Roombas introduced robot vacuums to consumers, there were some pitfalls that plagued the original Roombas. Because its navigation and cleaning algorithms depend on its sensors, the algorithms can only be as effective as the Roomba s sensors. Although there are IR transmitters for collision avoidance, one flaw with the Roomba s bump sensor system is that it is only activated once the Roomba has contacted an object. Because of the way the original Roomba s Bounce behavior works, this

6 would sometimes cause the Roomba to get stuck in a corner or between objects. Another flaw in the original Roombas is how the odometer worked. If the Roomba bumped into something, the wheels would slip and the Roomba would rotate, but this distance is not counted by the odometer. The odometer is used by the Roomba to calculate the tightness of its spiral when it is in Spot Coverage mode and to keep track of how it has traveled for its minimum and maximum distances in Wall-following mode. Thus, a miscalculation in the odometer could decrease the Roomba s cleaning efficiency. The 980 model improved on the older Roombas by implementing VSLAM into the Roomba, which allowed it to create a map of its environment and know where it is located. By doing so, the model does not need to rely solely on its sensors to operate, alleviating the 980 model of many of its flaws found in the older models. Another improvement the Roomba 980 has over its predecessors is that by incorporating VSLAM into its design and thus, creating a map of its surroundings, the 980 could resume cleaning where it left off after recharging, something the older models could not do. Although Roombas have improved moving from the original algorithms to VSLAM, some pitfalls still exist. The odometer issue is still a problem for the Roomba 980 since it combines the odometry data with its image data to create a map using VSLAM (Karlsson et al. 25). As well, the image data may be hard to collect and analyzed by the VSLAM algorithm since it relies on the quality of the images. The images may be blurry, have poor lighting (e.g. a dark room), etc., which may make the image data difficult for the Roomba 980 to use (Karlsson et al. 25). Consumer complaints have also plagued the original Roombas and the Roomba 980. One common complaint is that the Roomba is louder than what I expected (All Home Robotics). On the ethics side of the technology in Roombas, the cameras on the 980 model may present security and privacy concerns for consumers (Knight). Since irobot has expanded its robotics to military applications and since then have sold it off (Endeavor Robotics), we believe another ethical concern for the public is the military application of VSLAM in that VSLAM may be used to create deadly robots that can navigate in the environment on its own. While this may decrease the risk for human soldiers, the civilian risk is not necessarily reduced. Other Uses/Other Industries The Roomba is a great representation of how advances in technology and computational algorithms has led to more efficient products. In the world we live in today, people constantly attempt to optimize their daily tasks to achieve efficiency in their every-day lives and examples of these activities include: commuting to work using the shortest route, streamlining processes at work and at home to free up time, energy, and costs, or simply purchasing equipment or electronics that can perform a task at a much faster and cost-effective way (Yershov 1). Similarly, many industries are optimizing their products and making it greatly efficient by applying similar algorithmic techniques that we have observed in the Roomba 980. The field of robotics has been expanding dramatically from the 1990s through the present time. Early robots, most often found in the lab or factory environments, were expensive, hazardous, and difficult to operate (Yershov 1). Today, Computer Scientists have helped with advances in technology and algorithms used in motion planning, making robots both optimal and efficient. By studying the Roomba, we can determine that its technology can create an entrance into other industries and how it affects the performance of new products. Results demonstrate that, with some caveats, the Roomba is a viable foundation for both classroom and laboratory use, especially for work seeking to leverage robots to other ends, as well as robotics per se with a computational focus (Tribelhorn and Dodds 1393).

7 Gardening We have seen how effective the Roomba has become at performing arduous repetitive tasks in the household, but this technology can be applied to other similar procedures that are performed frequently, like gardening -weeding flowers or vegetable gardens. These procedures can then be carried out by a gardening rover. Like the mapping and navigation techniques already present in the Roomba, a gardening rover utilizes a path planning algorithm to detect plants and measure the environmental parameters of the area where they are growing, and after providing its attention, the rover moves to the nearest neighbouring plant based on the path planning algorithm (Kumar et al. 979). Along with this algorithm, computer scientists have also added important modifications to these gardening rovers to optimize its performance. A major addition is the feature extraction algorithm called SIFT (Scale and Invariant Feature Transform), which correctly identifies and classifies various plant species (Kumar et al. 979). This leads to a more efficient and effective care of the plants which in turn produces a higher quality yield. The term precision gardening is coined based on the use of such technology as the gardening rover is programmed to deliver the appropriate amount of water and fertilizer to enhance the growth of the plant (Kumar et al. 980). With algorithms that are homogenous to the ones in the Roomba and further modifications, gardening is becoming a much more efficient task which supplies high-quality outputs. Farming Given the right programming and equipped with the right tools, there is a competitive edge that robotics can have in larger industries like farming. With the increasing desire for organically grown produce, robotics can have a positive impact on the already time consuming and inefficient way in which these goods are produced. Organic farmers who want to weed their crops without the use of herbicides and other harmful chemicals go through a labor-intensive process of caring for their produce. Programming a self-navigating robot that can weed crops on a schedule, or even when they detect weeds on a route, can have a profound impact on the production of organically grown goods, on a global scale. Tractors, combine harvesters, and other farming equipment designed for specialized tasks are all human operated, time-consuming to use, and require specialized training. Robotics has the potential to play a major role in automating tasks on a farm. A driver-less tractor has already been introduced into the farming industry as a concept for the future of agriculture. Automatic tractor saves time, increases farm efficiency and lowers the overall cost. By calculating its current position and the path that it needs to follow, a robotic tractor can drive a precise path, perfectly matching rows to one another in the thickest fog or the dark of night. (Prakash et al. 1699). This provides a major advantage, not to mention the health and safety benefits, to farmers who are subject to adverse weather conditions or who simply cannot harvest their crops due to disabilities or illnesses. Self-driving vehicles The VSLAM algorithm is a crucial component that irobot has utilized in their Roombas and this technology is gradually expanding to several auto industries. Similar styles of the mapping and navigation technology that the VSLAM algorithm contains is being incorporated into vehicles for self-navigation. As

8 John Leonard, a professor at MIT, has remarked, VSLAM is becoming a valuable commercial tool which will potentially be used in self-driven cars (Knight). This statement reveals that the VSLAM algorithm is the platform for more efficient and futuristic technology. Self-driving cars utilize this algorithm to map its surroundings and sense any obstacles that are nearby (SLAM: Self-Driving Cars, AI and Mapping on the Move). Google s self-driving car is an example of a vehicle that uses a similar style of the VSLAM algorithm. Like the Roomba, Google s SLAM Technology operates by taking 3D images processed by a laser radar on top of their vehicle (Sandhu). The images are then used as measurements for the self-driving vehicle to make its driving decisions (Sandhu). The algorithm used in these self-driving cars are like the one in the Roomba in how they utilize their mapping and navigation techniques. As this technology continues to expand, we can expect future products to pilot us through our day to day lives (Sandhu). Conclusion irobot s Roomba vacuums are autonomous robots that have developed greatly with advances in technology. The original Roomba models consisted of various technologies and algorithms but it was evident that they had many limitations. To address these limitations, irobot released the Roomba 980 which equipped with improved technology and computational algorithms were able to upgrade the original Roombas and make them into an extremely efficient cleaning robots. Despite the advances in technology displayed through the Roomba 980, these robots still have several limitations. We have also found that further development in self-navigation and mapping technologies can lead to many different implementations in various industries. The effect of similar technology in the gardening, farming and selfdriving vehicle industries has contributed to increased productivity and efficiency in people's daily lives, turning arduous and labor intensive tasks into activities that can be programmed into autonomous robots. Overall, through our analysis on irobot s Roomba vacuums, we have discovered that technology always consists of limitations and that the expansion of technology and computer science in various industries will lead towards a more optimal society. However, we are left to wonder, will it ever be possible to achieve technology without limitations?

9 Works Cited Ackerman, Evan, and Enrico Guizzo. IRobot Brings Visual Mapping and Navigation to the Roomba 980. IEEE Spectrum: Technology, Engineering, and Science News, IEEE, 16 Sept. 2015, spectrum.ieee.org/automaton/robotics/home-robots/irobot-brings-visualmapping-and-navigation-to-the-roomba-980. Accessed 29 Mar Endeavor Robotics. Endeavor Robotics, Endeavor Robotics, endeavorrobotics.com/. Accessed 26 Mar Harvey, Brian, and Jens Monig. Snap! Reference Manual. Haselirad, Arad, and Jeremiah Neubert. An Algorithm for Semi-Autonomous Environment Navigation with Roomba. University of North Dakota, University of North Dakota, IRobot. IRobot, Accessed 23 Mar Jones, Joseph L., and Philip R. Mass. Method and System for Multi-Mode Coverage for an Autonomous Robot. 26 Oct Knight, Will. With a Roomba Capable of Navigation, IRobot Eyes Advanced Home Robots. MIT Technology Review, MIT Technology Review, 16 Sept. 2015, Accessed 20 Mar Kumar, V. Sathiesh, et al. Smart Autonomous Gardening Rover with Plant Recognition Using Neural Networks. Procedia Computer Science, vol. 93, 2016, pp , doi: /j.procs

10 Layton, Julia. How Robotic Vacuums Work. How Robotic Vacuums Work, HowStuffWorks, 3 Nov. 2005, electronics.howstuffworks.com/gadgets/home/robotic-vacuum.htm. Accessed 29 Mar Prakash, Neelam Rup, et al. An Autonomous Vehicle for Farming Using GPS. International Journal of Electronics and Computer Science Engineering, vol. 1, no. 3, 2012, pp Roomba 980 Robot 100x100.png. IRobot, IRobot, Accessed 25 Mar Roomba. Roomba, Omics International, research.omicsgroup.org/index.php/roomba. Accessed 29 Mar Sandhu, Robin. What Is SLAM Technology? Lifewire, About, Inc., 21 Aug. 2016, Accessed 20 Mar Sinclair, Patrick. Roomba 770 Review: Pros and Cons. Roomba 770 Review: Pros and Cons, All Home Robotics, 5 Dec. 2016, Accessed 29 Mar SLAM: Self-Driving Cars, AI and Mapping on the Move. SwiftKey Blog, Touchtype Ltd, 13 Aug. 2015, blog.swiftkey.com/slam-self-driving-cars-ai-and-mapping-on-the-move/. Accessed 22 Mar Tribelhorn, Ben, and Zachary Dodds. Evaluating the Roomba: A Low-Cost, Ubiquitous Platform for Robotics Research and Education. Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007, doi: /robot

11 Woodford, Chris. How Do Roomba Robot Vacuum Cleaners Work? Explain That Stuff, Explain That Stuff, 29 Nov. 2016, Accessed 26 Mar Yershov, Dmytro. Fast Numerical Algorithms for Optimal Robot Motion Planning. University of Illinois at Urbana-Champaign, 2013.