DocumentCode :
3315156
Title :
Randomized testing for Robotic plan execution for autonomous systems
Author :
Saigol, Zeyn ; Py, Frédéric ; Rajan, Kanna ; McGann, Conor ; Wyatt, Jeremy ; Dearden, Richard
Author_Institution :
Sch. of Comput. Sci., Univ. of Birmingham, Birmingham, UK
fYear :
2010
fDate :
1-3 Sept. 2010
Firstpage :
1
Lastpage :
9
Abstract :
Autonomous underwater vehicles (AUVs) are commonly used for carrying out pre-planned oceanographic surveys, but there is increasing interest in optimizing these surveys by performing onboard re- planning. MBARI has developed an advanced AUV control system, the Teleo Reactive Executive (T-REX) that enables the vehicle to survey areas in more detail if biogeochemical markers indicate the presence of a target feature, and even to follow dynamic ocean phenomena such as fronts. T-REX uses artificial intelligence (AI) techniques in constraint-based temporal planning together with a layered control architecture that allows plans to be generated and executed onboard. One challenge of onboard plan synthesis and execution is that the power of the system to generate different behaviors makes it hard to test in simulation, and failures at sea are costly. We introduce a randomized Monte-Carlo method based test approach that executes hundreds of simulated missions with each mission presenting different inputs to the planner, and checks each output plan for validity. The approach sets environmental parameters to exercise T-REX´s domain model, and it is fully configurable. We describe how the Monte-Carlo tester integrates with T-REX, how we have incorporated it into our testing process, and the benefits for system reliability that have resulted. We also highlight our experiences in discovering bugs both in simulation and for science surveys in waters off Northern California.
Keywords :
Monte Carlo methods; mobile robots; oceanographic techniques; planning (artificial intelligence); reliability; remotely operated vehicles; underwater vehicles; AUV control system; MBARI; T-REX domain model; artificial intelligence technique; autonomous underwater vehicle; biogeochemical marker; constraint based temporal planning; dynamic ocean phenomena; environmental parameter; layered control architecture; onboard replanning; preplanned oceanographic survey; randomized Monte Carlo method; randomized testing; robotic plan execution; simulation testing; system reliability; teleoreactive executive; Argon; Educational institutions; Gold; Software;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Autonomous Underwater Vehicles (AUV), 2010 IEEE/OES
Conference_Location :
Monterey, CA
ISSN :
1522-3167
Print_ISBN :
978-1-61284-980-5
Type :
conf
DOI :
10.1109/AUV.2010.5779648
Filename :
5779648
Link To Document :
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