cross-posted from willowgarage.com
Nobody likes to wait, even for a robot. So when a personal robot searches for an object to deliver, it should do so in a timely manner. To accomplish this, Feng Wu from the University of Science and Technology of China spent his summer internship developing new techniques to help PR2 select which sensors to use in order to more quickly find objects in indoor environnments.
Robots like PR2 have several sensors, but they can be generally categorized into two types: wide sensing and narrow sensing. Wide sensing covers larger areas and greater distances than narrow sensing, but the data may be less accurate. On the other hand, narrow sensing can use more power and take more time to collect and analyze. Feng worked on planning techniques to balance the tradeoffs between these two types of sensing actions, gathering more information while minimizing the cost.
The techniques involved the use of the Partially Observable Markov Decision Process. POMDP provides an ideal mathematical framework for modeling wide and narrow sensing. The sensing abilities and uncertainty of sensing data are modeled in an observation function. The cost of sensing actions can be defined in a reward function. The solution balances the costs of the sensing action and the rewards (i.e., the amount of information gathered).
For example, one part of the search tree might represent the following: "If I move to the middle of the room, look around and observe clutter to my left, then I will be very sure that there is a bottle to the left, moderately sure that there is nothing in front of me, and completely unsure about the rest of the room." When actually performing tasks in the world, the robot will receive observations from its sensors. These observations will be used to update its current beliefs. Then the robot can plan once again, using the updated beliefs. Planning in belief space allows making tradeoffs such as: "I'm currently uncertain about where things are, so it's worth taking some time to move to the center of the room to do a wide scan. Then I'll have enough information to choose a good location toward which to navigate."
For more information, you can read Feng's slides. You can also check out the find_object stack on ROS.org.
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