Probabilistic world modeling for distributed team planning
Chang, Mark & Wyeth, Gordon (2004) Probabilistic world modeling for distributed team planning. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Sendai, Japan, pp. 385-401.
This paper describes an application of decoupled probabilistic world modeling to achieve team planning. The research is based on the principle that the action selection mechanism of a member in a robot team can select an effective action if a global world model is available to all team members. In the real world, the sensors are imprecise, and are individual to each robot, hence providing each robot a partial and unique view about the environment. We address this problem by creating a probabilistic global view on each agent by combining the perceptual information from each robot. This probabilistic view forms the basis for selecting actions to achieve the team goal in a dynamic environment. Experiments have been carried out to investigate the effectiveness of this principle using custom-built robots for real world performance, in addition, to extensive simulation results. The results show an improvement in team effectiveness when using probabilistic world modeling based on perception sharing for team planning.
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|Item Type:||Conference Paper|
|Keywords:||mobile robots, multi-robot systems, pobability|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)|
|Deposited On:||24 Jun 2010 00:18|
|Last Modified:||10 Aug 2011 13:31|
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