随着无人机监测、巡查和测绘等低空技术得到广泛应用,低空长距离空中路径规划成为低空航空器应用面临的一个挑战。而传统快速扩展随机树(Rapidly-Exploring Random Trees/RRT)及其改进算法在大范围长距离低空三维空间下面临计算效率慢...随着无人机监测、巡查和测绘等低空技术得到广泛应用,低空长距离空中路径规划成为低空航空器应用面临的一个挑战。而传统快速扩展随机树(Rapidly-Exploring Random Trees/RRT)及其改进算法在大范围长距离低空三维空间下面临计算效率慢的问题,对此,本文提出一种带有R树空间索引的双向启发式RRT^(*)算法,该算法在双向RRT^(*)算法基础上为随机采样过程设置了启发函数,使得在面对狭小城市障碍物之间空隙时,能够避免局部最小值情况的出现。在此基础上为城市障碍物建立R树空间索引,减少了海量障碍物情况下碰撞检测的时间,提高了低空长距离空中路径规划效率。此外,为了得到更加符合无人机运动规律的路径,提高算法的实用性,在采样过程中设置转弯阈值控制转弯角度,并且对规划结果路径使用3次B-spline函数进行路径平滑。最后在武汉市三维城市场景中,利用武汉市建筑物数据进行了实验,实验证明相比已有算法,本文提出的带有R树空间索引的双向启发式RRT^(*)算法相比较RRT算法和双向RRT^(*)算法在500 m、2000 m、10000 m不同距离下规划时间均降低了90%以上;采样次数相比RRT算法在不同距离下分别降低了51.6%、75%、86.7%,相比双向RRT^(*)算法在不同距离下分别降低了20%、24.7%、57.3%;转弯次数相比RRT算法在不同距离下分别降低了77.3%、73.5%、78.3%,相比双向RRT^(*)算法在不同距离下分别降低了37.5%、30.8%、16.8%;同时带有R树空间索引的双向启发式RRT^(*)算法得到的结果路径长度相比其他2种算法也有缩短。该算法应用于低空长距离空中路径规划能够有效提高计算效率,降低规划时间,减少采样次数,缩短结果路径,减少转弯次数,丰富无人机的应用场景。展开更多
With the increasing complexity of substation inspection tasks,achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional(3D)environments rem...With the increasing complexity of substation inspection tasks,achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional(3D)environments remains a critical challenge.To address this problem,this paper proposes an improved path planning algorithm—Random Geometric Graph(RGG)-guided Rapidly-exploring Random Tree(R-RRT)—based on the classical Rapidly-exploring Random Tree(RRT)framework.First,a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering,noise removal,coordinate transformation,and obstacle inflation using spherical structuring elements.During the planning stage,a dynamic goal-biasing strategy is introduced to adaptively adjust the sampling direction,the sampling distribution is optimized using a pre-generated RGG,and collision detection is accelerated via a K-Dimensional Tree structure.After initial trajectory generation,redundant nodes are eliminated via greedy pruning,and a curvature-minimizing gradient-based optimizationmethod is applied to smooth the trajectory.Experimental results conducted in a simulated substation environment demonstrate that,compared with mainstream path planning algorithms,the proposed R-RRT achieves superior performance in terms of path length,planning time,and trajectory smoothness.Comprehensive analysis shows that the proposed method significantly enhances trajectory quality,planning efficiency,and operational safety,validating its applicability and advantages for high-precision 3D path planning in complex substation inspection scenarios.展开更多
基金Funding for this research was provided by the Program for Scientific Research Innovation Team in Colleges and Universities of Anhui Province(No.2022AH010095)the Hefei Key Technology R&D“Champion-Based Selection”Project(No.2023SGJ011).
文摘With the increasing complexity of substation inspection tasks,achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional(3D)environments remains a critical challenge.To address this problem,this paper proposes an improved path planning algorithm—Random Geometric Graph(RGG)-guided Rapidly-exploring Random Tree(R-RRT)—based on the classical Rapidly-exploring Random Tree(RRT)framework.First,a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering,noise removal,coordinate transformation,and obstacle inflation using spherical structuring elements.During the planning stage,a dynamic goal-biasing strategy is introduced to adaptively adjust the sampling direction,the sampling distribution is optimized using a pre-generated RGG,and collision detection is accelerated via a K-Dimensional Tree structure.After initial trajectory generation,redundant nodes are eliminated via greedy pruning,and a curvature-minimizing gradient-based optimizationmethod is applied to smooth the trajectory.Experimental results conducted in a simulated substation environment demonstrate that,compared with mainstream path planning algorithms,the proposed R-RRT achieves superior performance in terms of path length,planning time,and trajectory smoothness.Comprehensive analysis shows that the proposed method significantly enhances trajectory quality,planning efficiency,and operational safety,validating its applicability and advantages for high-precision 3D path planning in complex substation inspection scenarios.
