Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task

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• 作者：Yongbo Chen ^{bit_chenyongbo@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jianqiao Yu ; ^{jianqiao@bit.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yuesong Mei ^{mys001@bit.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Siyu Zhang ^{ylww@bit.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xiaolin Ai ^{bitaixiaolin@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Zhenyue Jia ^{jiazhenyue@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Hierarchic optimization strategy ; Parallel CFO-GA algorithm ; Path planning ; Six-degree-of-freedom rigid-body dynamic model ; Trajectory optimization ; Trajectory planning
• 刊名：Chinese Journal of Aeronautics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：29
• 期：1
• 页码：184-201
• 全文大小：2770 K

文摘

A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA), which combines the central force optimization (CFO) algorithm with the genetic algorithm (GA). Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time systems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP) algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time performance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time.