Have you ever watched a YouTube video of a cat hitching a ride on a Roomba robot vacuum cleaner and wondered if the Roomba is actually able to perform its job properly while it has a cat sitting on its back? I blame my thesis research for the fact that I've thought about these things...
Robots find their way around a room by using sensors to perceive the world in front of them, and reconcile this view against a map of the environment to calculate their current position in the environment. The Roomba needs an estimate of its current location in the environment so that it can plan a driving route that will help it achieve its goal of vacuuming under the couch. This location estimate is like a "You Are Here" sticker on the map at a shopping mall. Abnormal driving events like a cat sitting on a Roomba or a person picking up a robot and putting it down somewhere else have the same effect as if the "You Are Here" sticker were moved to some completely wrong location on the shopping mall map. With an incorrect location estimate, the robot becomes unable to plan a path that will allow it to reach its goal. These abnormal driving conditions are also known as Robot Kidnapping Scenarios. These kidnapping scenarios can be detected and corrected to ensure the robot's success even under abnormal situations.
Existing methods of Kidnap Detection have two flaws: some methods are built to detect only certain types of Kidnapping scenarios. For example, a method might be built to detect situations in which a person picks up the robot, but the method is unable to detect that a cat is sitting on the robot. Other methods require specific sensors or large processing power requirements, which prevents the easily implementation of these methods on just any robot configuration.
For my thesis research, I developed a method to detect Robot Kidnapping events that is more flexible than existing methods. My detection scheme is designed to combat the weaknesses of existing methods by detecting any form of Robot Kidnapping on any robot. To accomplish this goal, I am re-purposing some of the data that is generated in the normal course of the robot's localization process. This re-purposing means there are no additional resource or configuration requirements to run this detection method.
To study this problem, I ran 40 simulations of a robot driving around a room. In the top example of the slide, the robot was driving under normal circumstances. As you can see, the algorithm tracking the robot's location represents the robot's possible true location as a cloud of points. Each point is associated with a confidence measure to represent the likelihood that this position is the robot's true current location. On the slide, the orange values are the most likely, while the blue values are the least likely. The main takeaway is that under normal circumstances, the confidences are spread across a spectrum of values.
The lower section of the slide shows the data immediately following a Kidnapping Event, when I picked up the robot and moved it to a new location. The localization algorithm naturally flattened out the variation in the confidence values, and this is represented in the plain green cloud on the graph.
This insight can be translated into the 10-bin histograms as seen on the right side of the slide. The skewed plot of the Kidnapped example was used to train an artificial neural network function to recognize the skew as an indicator that the robot has been Kidnapped. This method is 92% accurate and can identify Kidnapping Events within 3 update cycles of the localization algorithm.
Thanks to this system, cats can continue to enjoy Roombas, and now Roombas can get all of their work done.
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