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Adaptive agent tracking approach for oil contamination in water environments
International Journal of Advanced Robotic Systems ( IF 2.3 ) Pub Date : 2020-07-01 , DOI: 10.1177/1729881420940217 Xiaoci Huang 1, 2 , Jianjun Yi 1 , Yang Chen 2 , Xiaomin Zhu 1 , Zhiyong Dai 1
International Journal of Advanced Robotic Systems ( IF 2.3 ) Pub Date : 2020-07-01 , DOI: 10.1177/1729881420940217 Xiaoci Huang 1, 2 , Jianjun Yi 1 , Yang Chen 2 , Xiaomin Zhu 1 , Zhiyong Dai 1
Affiliation
The online monitoring of water environments is urgently needed. A feasible and effective approach is the use of agents. Water environments, similar to other real-world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, agents should be prepared to deal with various situations. In this study, we focused on an adaptive agent tracking approach for oil contamination. An integrated tracking framework, which is used to track the moving contour of oil pollution via a system comprising multiple unmanned surface vehicles, is proposed. The zigzag, unmanned underwater vehicle-gas, cloverleaf trajectory and curvature-weighted deployment algorithm methods are employed with consideration of their suitability to our approach. A cyclic particle swarm optimisation–Kalman method is also proposed. The possible position of moving vertices is predicted by the Kalman filter, and an objective search region is generated around the centre position. Moreover, particle swarm optimisation is performed to search for the best target position in this region. This particle swarm optimisation–Kalman method is circle operated to compensate for the deficiency of a few agents. To evaluate the approach, we conduct usability and performance simulations.
中文翻译:
水环境中油污染的自适应代理跟踪方法
迫切需要水环境在线监测。一种可行且有效的方法是使用代理。与其他现实世界环境类似,水环境呈现出许多变化和不可预测的情况。为了确保在这种环境中的灵活性,代理应该准备好处理各种情况。在这项研究中,我们专注于油污染的自适应代理跟踪方法。提出了一种集成跟踪框架,用于通过由多个无人水面车辆组成的系统跟踪油污的移动轮廓。考虑到它们对我们方法的适用性,采用了锯齿形、无人水下航行器-气体、三叶草轨迹和曲率加权部署算法方法。还提出了循环粒子群优化-卡尔曼方法。移动顶点的可能位置由卡尔曼滤波器预测,并围绕中心位置生成目标搜索区域。此外,执行粒子群优化以搜索该区域中的最佳目标位置。这种粒子群优化-卡尔曼方法是循环操作的,以弥补一些代理的不足。为了评估该方法,我们进行了可用性和性能模拟。
更新日期:2020-07-01
中文翻译:
水环境中油污染的自适应代理跟踪方法
迫切需要水环境在线监测。一种可行且有效的方法是使用代理。与其他现实世界环境类似,水环境呈现出许多变化和不可预测的情况。为了确保在这种环境中的灵活性,代理应该准备好处理各种情况。在这项研究中,我们专注于油污染的自适应代理跟踪方法。提出了一种集成跟踪框架,用于通过由多个无人水面车辆组成的系统跟踪油污的移动轮廓。考虑到它们对我们方法的适用性,采用了锯齿形、无人水下航行器-气体、三叶草轨迹和曲率加权部署算法方法。还提出了循环粒子群优化-卡尔曼方法。移动顶点的可能位置由卡尔曼滤波器预测,并围绕中心位置生成目标搜索区域。此外,执行粒子群优化以搜索该区域中的最佳目标位置。这种粒子群优化-卡尔曼方法是循环操作的,以弥补一些代理的不足。为了评估该方法,我们进行了可用性和性能模拟。