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Image Understanding and Knowledge-Based Systems

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Probabilistic, Prediction-based Schedule Debugging for Autonomous Robot Office Couriers (bibtex)
Probabilistic, Prediction-based Schedule Debugging for Autonomous Robot Office Couriers (bibtex)
by M Beetz, M Bennewitz and H Grosskreutz
Abstract:
Acting efficiently and meeting deadlines requires autonomous robots to schedule their activities. It also requires them to act flexibly: to exploit opportunities and avoid problems as they occur. Scheduling activities to meet these requirements is an important research problem in its own right. In addition, it provides us with a problem domain where modern symbolic AI planning techniques can enable robots to exhibit better performance than they possibly could without planning. This paper describes PPSD, a novel planning technique that enables autonomous robots to impose order constraints on concurrent percept-driven plans to increase the plans' efficiency. The basic idea is to generate a schedule under simplified conditions and then to iteratively detect, diagnose, and eliminate behavior flaws caused by the schedule based on a small number of randomly sampled symbolic execution scenarios. The paper discusses the integration of PPSD into the controller of an autonomous robot office courier and gives an example of its use.
Reference:
Probabilistic, Prediction-based Schedule Debugging for Autonomous Robot Office Couriers (M Beetz, M Bennewitz and H Grosskreutz), In Proceedings of the 23rd German Conference on Artificial Intelligence (KI 99), Springer Verlag, 1999. 
Bibtex Entry:
@inproceedings{beetz_probabilistic_1999,
 author = {M Beetz and M Bennewitz and H Grosskreutz},
 title = {Probabilistic, Prediction-based Schedule Debugging for Autonomous
	Robot Office Couriers},
 booktitle = {Proceedings of the 23rd German Conference on Artificial Intelligence
	({KI} 99)},
 year = {1999},
 address = {Bonn, Germany},
 publisher = {Springer Verlag},
 abstract = {Acting efficiently and meeting deadlines requires autonomous robots
	to schedule their activities. It also requires them to act flexibly:
	to exploit opportunities and avoid problems as they occur. Scheduling
	activities to meet these requirements is an important research problem
	in its own right. In addition, it provides us with a problem domain
	where modern symbolic {AI} planning techniques can enable robots
	to exhibit better performance than they possibly could without planning.
	This paper describes {PPSD}, a novel planning technique that enables
	autonomous robots to impose order constraints on concurrent percept-driven
	plans to increase the plans' efficiency. The basic idea is to generate
	a schedule under simplified conditions and then to iteratively detect,
	diagnose, and eliminate behavior flaws caused by the schedule based
	on a small number of randomly sampled symbolic execution scenarios.
	The paper discusses the integration of {PPSD} into the controller
	of an autonomous robot office courier and gives an example of its
	use.},
}
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Informatik IX

Image Understanding and Knowledge-Based Systems

Boltzmannstrasse 3
85748 Garching

info@iuks.in.tum.de