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A Predictive Guidance Obstacle Avoidance Algorithm for AUV in Unknown Environments

机译:未知环境中AUV的预测性制导避障算法

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摘要

A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the corresponding obstacle avoidance rules are formulated. Lastly, combining with the obstacle avoidance parameters and rules, the AUV’s predicting obstacle avoidance trajectory point is obtained. Then AUV can successfully achieve obstacle avoidance using the guidance algorithm. The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments. Lastly, comparing the execution efficiency and cost of different algorithms, which deal with various complex obstacle environments, simulation experiment results indicate the high efficiency and great adaptability of the proposed algorithm.
机译:针对必须适应多种复杂障碍环境的自动水下航行器(AUV),提出了未知环境下的预测性引导避障算法(PGOA)。利用前视声纳(FLS)收集的环境信息,通过凸算法和贝塞尔插值简化了障碍物边界。结合预测控制二次优化函数和避障权重函数,获得了避障轨迹的预测参数。根据障碍物环境的不同类型,制定相应的避障规则。最后,结合避障参数和规则,获得了AUV的避障预测轨迹。然后,AUV可以使用制导算法成功实现避障。仿真结果表明,PGOA算法可以更好地预测AUV避障路径的轨迹点,并且二次优化功能可以成功实现针对不同复杂障碍环境的避碰。最后,通过比较处理各种复杂障碍环境的不同算法的执行效率和成本,仿真实验结果表明该算法具有较高的效率和较大的适应性。

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