In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone ...In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.展开更多
This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual fo...This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual force （VF） is constructed and the corresponding optimal solving method under the given indicators is presented. Specifically,a fixed step method is developed to reduce computational cost and the reachable condition of path planning is proved. The Bayesian belief network and fuzzy logic reasoning theories are applied to setting the path planning parameters adaptively,which can reflect the battlefield situation dy-namically and precisely. A new way of combining threats is proposed to solve the local minima problem completely. Simulation results prove the feasibility and usefulness of using FVF for UAV path planning. Performance comparisons between the FVF method and the A＊ search algorithm demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems.展开更多
针对多无人艇编队避障问题,对静态避障的路径消耗问题进行建模分析,在动态避障时提出一种偏置人工势场法使策略符合艇群国际海上避碰规则(swarm International Regulations for Preventing Collisions at Sea,sCOLREGS)。本方法首先对...针对多无人艇编队避障问题,对静态避障的路径消耗问题进行建模分析,在动态避障时提出一种偏置人工势场法使策略符合艇群国际海上避碰规则(swarm International Regulations for Preventing Collisions at Sea,sCOLREGS)。本方法首先对传统人工势场法进行改进,定义符合艇群会遇态势判断需求的sCOLREGS,通过速度障碍法实时判断碰撞风险,然后利用偏置斥力区域的改进人工势场法实现对规则的遵守。仿真实验表明,本文方法在障碍物与编队大小相当时可显著减少避障路程,在确保避障实时性的同时,较好地遵守了国际海上避碰规则相关条例。研究结论可为海面无人艇集群安全航行提供参考。展开更多
针对无人车在复杂环境中进行全局路径规划时存在的盲目搜索、节点冗余、路径不光滑及不安全等问题,提出一种基于快速扩展随机树(RRT,rapidly-exploring random tree)的综合改进路径规划算法;首先引入目标动态概率采样策略和人工势场引...针对无人车在复杂环境中进行全局路径规划时存在的盲目搜索、节点冗余、路径不光滑及不安全等问题,提出一种基于快速扩展随机树(RRT,rapidly-exploring random tree)的综合改进路径规划算法;首先引入目标动态概率采样策略和人工势场引导随机树扩展机制;其次根据汽车运动学模型,对规划的路径进行转角约束和碰撞检测,保证路径的安全性;然后引入Reeds-Sheep曲线用于直接与目标位姿进行连接,避免多余的位姿调整;最后对路径进行剪枝和平滑处理,得到一条更短更光滑的路径;在实验部分,针对不同仿真环境,以规划时间、路径长度和节点数目作为评价指标,对比了RRT算法、RRT*算法和文章算法的路径规划效果;实验结果显示,文章算法相比于RRT算法和RRT*算法,节点数目分别减少了58.94%和85.22%,规划时间分别缩短了61.20%和79.23%,且路径长度相比于RRT算法缩短了17.26%,并和RRT*算法规划的最优路径长度相近。展开更多
基金This work was supported by the Postdoctoral Fund of FDCT,Macao(Grant No.0003/2021/APD).Any opinions,findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.
文摘In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.
基金National Natural Science Foundation of China (60975073)Aeronautical Science Foundation of China (2008ZC13011)+1 种基金Research Foundation for Doctoral Program of Higher Education of China (20091102110006)Fundamental Research Funds for the Central Universities
文摘This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual force （VF） is constructed and the corresponding optimal solving method under the given indicators is presented. Specifically,a fixed step method is developed to reduce computational cost and the reachable condition of path planning is proved. The Bayesian belief network and fuzzy logic reasoning theories are applied to setting the path planning parameters adaptively,which can reflect the battlefield situation dy-namically and precisely. A new way of combining threats is proposed to solve the local minima problem completely. Simulation results prove the feasibility and usefulness of using FVF for UAV path planning. Performance comparisons between the FVF method and the A＊ search algorithm demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems.
文摘针对多无人艇编队避障问题,对静态避障的路径消耗问题进行建模分析,在动态避障时提出一种偏置人工势场法使策略符合艇群国际海上避碰规则(swarm International Regulations for Preventing Collisions at Sea,sCOLREGS)。本方法首先对传统人工势场法进行改进,定义符合艇群会遇态势判断需求的sCOLREGS,通过速度障碍法实时判断碰撞风险,然后利用偏置斥力区域的改进人工势场法实现对规则的遵守。仿真实验表明,本文方法在障碍物与编队大小相当时可显著减少避障路程,在确保避障实时性的同时,较好地遵守了国际海上避碰规则相关条例。研究结论可为海面无人艇集群安全航行提供参考。
文摘针对无人车在复杂环境中进行全局路径规划时存在的盲目搜索、节点冗余、路径不光滑及不安全等问题,提出一种基于快速扩展随机树(RRT,rapidly-exploring random tree)的综合改进路径规划算法;首先引入目标动态概率采样策略和人工势场引导随机树扩展机制;其次根据汽车运动学模型,对规划的路径进行转角约束和碰撞检测,保证路径的安全性;然后引入Reeds-Sheep曲线用于直接与目标位姿进行连接,避免多余的位姿调整;最后对路径进行剪枝和平滑处理,得到一条更短更光滑的路径;在实验部分,针对不同仿真环境,以规划时间、路径长度和节点数目作为评价指标,对比了RRT算法、RRT*算法和文章算法的路径规划效果;实验结果显示,文章算法相比于RRT算法和RRT*算法,节点数目分别减少了58.94%和85.22%,规划时间分别缩短了61.20%和79.23%,且路径长度相比于RRT算法缩短了17.26%,并和RRT*算法规划的最优路径长度相近。
