城市区域物流无人机路径规划

针对城市区域物流无人机路径规划问题,采用栅格法进行环境建模,综合考虑无人机性能、任务性质和城市环境等影响要素,以航程、高度变化和危险度最小为目标函数,构建多约束物流无人机路径规划模型.改进A*(A-star)算法求解：为合理预估距离,采用欧氏距离与曼哈顿距离线性组合的方式设计启发函数;为提高搜索效率,引入双向搜索策略;为保证平稳飞行,采用B样条(B-spline)法进行路径优化.结果表明：模型可以实现多目标优化,具有有效性;算法与传统A*算法相比,规划时间少,规划路径航程短,高度变化少,飞行安全稳定.分析参数权重值得出：当3个子目标代价权重系数分别为0.4、0.1和0.5,2种距离权重系数分别为0.15和0.85时,规划路径最优.

Path Planning for Logistics Unmanned Aerial Vehicle in Urban Area

This paper solves the path planning problem of logistics unmanned aerial vehicle (UAV) in the urban area. The grid method is first used to model the environment with the performance of UAV, task nature, and urban environment. A multi-constrained logistics UAV path planning model is then constructed to minimize the range, height change, and risk. And an improved A* algorithm is designed to solve the model. The heuristic function uses the linear combination of Euclidean distance and Manhattan distance to estimate the distance reasonably. A twoway search strategy is introduced to improve the search efficiency. The B-spline method is applied to optimize the path in order to ensure a smooth flight. The research results show that the proposed model is effective to achieve multi-objective performance. Compared with the traditional A* algorithm, the improved algorithm can solve for a shorter path with less altitude change and stable flight safety in less computational time. When the weight coefficients of the three sub-targets are 0.4, 0.1, and 0.5 respectively, and the weights of the two distances are 0.15 and 0.85 respectively, the UAV path planned by this algorithm is the best.