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Multi-phase trajectory optimization for an aerial-aquatic vehicle considering the influence of navigation error

机译：考虑导航误差影响的水上飞行器多相轨迹优化

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摘要

The environment-induced multi-phase trajectory optimization problem is studied in this paper, and the underwater target tracking task is focused on. The task is finished by an aerial-aquatic coaxial eight-rotor vehicle and is divided into two phases, i.e., the diving phase and the underwater navigation phase. The dynamic model and constraints on angular velocity of rotor in each phase are established to understand the motion characteristic. Then the model of navigation information and terrain matching are contained in the trajectory optimization model to reflect the influence of underwater navigation error on the quality of trajectory. Correspondingly, the forms of collision detection and cost function are changed to adapt to the inaccurate navigation information. To obtain the trajectory with the minimum terminal position error, an improved teach & leam-based optimization (ITLBO) algorithm is developed to strengthen the influence of individual historical optimal solution. Besides, Chebyshev collocation points are applied to determine the locations of control variables. Simulation results demonstrate that the established navigation error-based trajectory optimization model can reflect the real situation of multi-phase task. Especially, it is able to calculate the collision probability between the vehicle and the obstacle when GPS is unavailable underwater, thus ensuring the safety of underwater navigation. Compare to other common effective algorithms, the proposed ITLBO algorithm is in general more suitable for solving this problem because it is swarm-based and can obtain good solution without worrying about the inappropriate values of user-defined parameters.
机译：研究了环境引起的多相轨迹优化问题，并重点研究了水下目标跟踪任务。该任务由水上同轴八旋翼飞行器完成，分为两个阶段，即潜水阶段和水下航行阶段。建立了各阶段转子角速度的动力学模型和约束条件，以了解运动特性。然后将导航信息和地形匹配模型包含在轨迹优化模型中，以反映水下导航误差对轨迹质量的影响。相应地，改变了碰撞检测的形式和成本函数以适应不准确的导航信息。为了获得具有最小终端位置误差的轨迹，开发了一种改进的基于示教和学习的优化（ITLBO）算法，以增强各个历史最优解的影响。此外，使用切比雪夫搭配点来确定控制变量的位置。仿真结果表明，所建立的基于导航误差的轨迹优化模型能够反映多阶段任务的真实情况。尤其是能够在水下无法获得GPS时计算车辆与障碍物之间的碰撞概率，从而确保了水下航行的安全性。与其他常见的有效算法相比，所提出的ITLBO算法通常更适合解决此问题，因为它是基于群体的算法，可以得到良好的解决方案，而不必担心用户定义的参数的不适当值。

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