Optimal Coordination of Multi-task Allocation and Path Planning for UAVs Using Dynamic Bayesian Network
A key challenge for the Unmanned Aerial Vehicles (UAVs) is to develop an overall system architecture that can perform optimal coordination of the UAVs and reconfigure to account for changes in the dynamic environment with uncertainty. This paper presents a multi-task allocation and path planning optimal coordination algorithm for UAVs based on Dynamic Bayesian Network (DBN) perceiving architecture, which leads to solve above autonomous problems in dynamic aerospace surroundings. Learning and inference will be based on Bayesian approach, by representing uncertainty in observed data, and by using probability techniques to compute the goal attributes given the observation data. Under given missions and guidelines, learning, inference and prediction can be carried out by the same principle and these clarify the new direction for the decision-making optimization. The valid overall approach is demonstrated on example scenarios which show that, during execution, the coordination tasks of multi-task allocation and path planning for UAVs, which react to changes in the dynamic aerospace environments, can be achieved autonomously.
Optimal Coordination Multi-task Allocation Planning Dynamic Bayesian Network
Guo Wen-Qiang Hou Yong-yan
College of Electrical and Info. Engineering, Shaanxi Univ. of Science and Technology, Xi’an, 710021, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
3590-3594
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）