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首页> 外文期刊>Journal of aerospace engineering >Distributed Cooperative Search with Collision Avoidance for a Team of Unmanned Aerial Vehicles Using Gradient Optimization
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Distributed Cooperative Search with Collision Avoidance for a Team of Unmanned Aerial Vehicles Using Gradient Optimization

机译：梯度优化的无人飞行器团队避碰分布式协作搜索

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This paper addresses the cooperative search problem for a team of unmanned aerial vehicles (UAVs) with limited field-of-view (FOV) and available overload constraints. First, the models of environment, UAV, image sensor, and communication are established. Second, a modified distributed information fusion strategy based on the Bayesian rule is proposed, which can make all the individual probability maps converge to the same one and reflect the true existence or nonexistence of targets within each cell. Third, a distributed gradient-based optimization method for path planning involved in the cooperative search is proposed and the available overload constraint of the UAV can be taken into account. Fourth, the aforementioned distributed gradient-based optimization method is improved by involving collision avoidance, establishing reasonable safety distance constraints, and using the Lagrange multiplier. Finally, the correctness and effectiveness of the proposed method are validated and demonstrated via simulations. (C) 2016 American Society of Civil Engineers.
机译：本文针对具有有限视场（FOV）和可用过载限制的无人飞行器（UAV）团队解决了协同搜索问题。首先，建立环境，无人机，图像传感器和通信的模型。其次，提出了一种改进的基于贝叶斯规则的分布式信息融合策略，该策略可以使所有个体概率图收敛于同一图，并反映每个单元中目标的真实存在与否。第三，提出了一种基于梯度的分布式优化方法，用于协同搜索中的路径规划，可以考虑无人机的可用过载约束。第四，通过涉及避免碰撞，建立合理的安全距离约束并使用拉格朗日乘数来改进上述基于分布式梯度的优化方法。最后，通过仿真验证了所提方法的正确性和有效性。 （C）2016年美国土木工程师学会。

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