Title :
Autonomous underwater vehicle: Petri net based hybrid control of mission and motion
Author :
Chang, Zong-Hu ; Xin-Qian Bian ; Shi, Xiao-cheng
Author_Institution :
Harbin Eng. Univ., China
Abstract :
Terrain scanning is one of important missions of autonomous underwater vehicle. AUVs mission control covers a wide spectrum of research topics focusing on the interplay between event-driven and time-driven dynamical systems. The former is within the realm of discrete-event system theory, whereas the latter can be tackled using well-established theoretical tools from the field of continuous- and discrete-time dynamical system. The paper describes behavior arbitration hybrid software architecture of the mission control procedure which composes of mission level, task level and behavior level. The paper provides a mission control procedure model by adopting the formalism of extended Petri net theory. The task coordination algorithm based on the RW discrete event system theory has also been proposed to coordinate the pre-planned tasks and newly triggered tasks according to the inner or external events. Details are given about the autonomous underwater vehicle at the present time, together with the simulation experimental validation of terrain scanning mission in the virtual system which has shown the reasonable hardware system, the implementation of the software and the correctness of the task coordination algorithm.
Keywords :
Petri nets; control engineering computing; discrete event systems; discrete time systems; motion control; remotely operated vehicles; software architecture; underwater vehicles; Petri net; autonomous underwater vehicle; discrete-event dynamical system; discrete-time dynamical system; hybrid software architecture; mission control; motion control; task coordination algorithm; terrain scanning; Automotive engineering; Communication system control; Control systems; Discrete event systems; Hardware; Motion control; Software algorithms; Software architecture; Underwater vehicles; Vehicle driving;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1382356