LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

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.

Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Path Planning Method Based on D^(*) lite Algorithm for Unmanned Surface Vehicles in Complex Environments

In recent decades,path planning for unmanned surface vehicles(USVs)in complex environments,such as harbours and coastlines,has become an important concern.The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles.In this study,we developed a novel path planning method based on the D^(*) lite algorithm for real-time path planning of USVs in complex environments.The proposed method has the following advantages:(1)the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles;(2)a constrained artificial potential field method is employed to enhance the safety of the planned paths;and(3)the method is practical in terms of vehicle performance.The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms.The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.

Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles

This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV.

Improved lazy theta algorithm based on octree map for path planning of UAV

This paper investigates the path planning method of unmanned aerial vehicle(UAV)in threedimensional map.Firstly,in order to keep a safe distance between UAV and obstacles,the obstacle grid in the map is expanded.By using the data structure of octree,the octree map is constructed,and the search nodes is significantly reduced.Then,the lazy theta*algorithm,including neighbor node search,line-of-sight algorithm and heuristics weight adjustment is improved.In the process of node search,UAV constraint conditions are considered to ensure the planned path is actually flyable.The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes.Heuristic weight adjustment strategy is employed to control the precision and speed of search.Finally,the simulation results show that the improved lazy theta*algorithm is suitable for path planning of UAV in complex environment with multi-constraints.The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Global path planning of unmanned vehicle based on fusion of A*algorithm and Voronoi field

Purpose–Since many global path planning algorithms cannot achieve the planned path with both safety and economy,this study aims to propose a path planning method for unmanned vehicles with a controllable distance from obstacles.Design/methodology/approach–First,combining satellite image and the Voronoi field algorithm(VFA)generates rasterized environmental information and establishes navigation area boundary.Second,establishing a hazard function associated with navigation area boundary improves the evaluation function of the A*algorithm and uses the improved A*algorithm for global path planning.Finally,to reduce the number of redundant nodes in the planned path and smooth the path,node optimization and gradient descent method(GDM)are used.Then,a continuous smooth path that meets the actual navigation requirements of unmanned vehicle is obtained.Findings–The simulation experiment proved that the proposed global path planning method can realize the control of the distance between the planned path and the obstacle by setting different navigation area boundaries.The node reduction rate is between 33.52%and 73.15%,and the smoothness meets the navigation requirements.This method is reasonable and effective in the global path planning process of unmanned vehicle and can provide reference to unmanned vehicles’autonomous obstacle avoidance decision-making.Originality/value–This study establishes navigation area boundary for the environment based on the VFA and uses the improved Aalgorithm to generate a navigation path that takes into account both safety and economy.This study also proposes a method to solve the redundancy of grid environment path nodes and large-angle steering and to smooth the path to improve the applicability of the proposed global path planning method.The proposed global path planning method solves the requirements of path safety and smoothness.

基于A^(*)-动态窗口法的无人船动态路径规划算法

为使无人船在复杂的海上环境具备自主路径规划能力,保证其能够在未知环境中避开障碍物并准确到达目的地,结合A^(*)算法的全局最优特性和动态窗口法的实时性,提出了一种新的无人船动态路径规划算法。将A^(*)算法规划的路径点作为动态窗口法的局部目标点,并在中间路径点不可达时及时重新规划全局路径,形成了A^(*)-动态窗口法。动态环境下的仿真结果表明:该融合算法能引导无人船通过平滑的路径顺利到达目标点,证明了该算法的合理性和有效性,而与多种传统算法的对比结果,验证了所提A^(*)-动态窗口法的优越性。

复杂城市低空无人机安全风险评估与三维路径规划

针对复杂城市环境内低空无人机飞行安全与效率亟待提升的问题,提出了复杂城市低空无人机安全风险评估与三维路径规划方法。首先,设计了无人机越界冲突率、缓冲空域占比指标,建立了无人机地理围栏安全缓冲间距优化模型,对最佳缓冲间距和栅格粒度进行了标定;然后,构建了由人口密度层、遮蔽层和障碍层构成的无人机风险地图,建立了弹道下降和失控滑行两种模式下的无人机对地风险评估模型,生成了精细化、组合化的城市低空概率风险地图;最后,综合利用地理围栏、概率风险地图和跳点搜索算法,对无人机三维路径进行了初始规划和优化重构。结果表明:弹道下降模式的伤亡风险是失控滑行下降模式的5~75倍;与A*算法相比,跳点搜索算法有效减少了飞行路径的转弯数量,缩短了求解时长,更适合规划无人机飞行路径;与不采用风险地图的方法相比,基于风险地图的无人机路径规划减少了50%的较高风险节点,相应的路径长度仅增加了7.2%和11.4%,整体路径节点的伤亡风险明显降低。研究成果可为复杂城市低空无人机飞行计划制定及安全运行监管提供理论依据和方法支撑。

旅行商和覆盖路径规划问题的自适应遗传算法

针对无人机在区域覆盖中的路径规划问题,对于有障碍物的环境,一种有效方法是将环境分解为多个区域,覆盖所有区域即覆盖整个环境。访问多个区域并回到原点的最短路径看做旅行商问题(TSP),每个区域内部为覆盖路径规划问题(CPP),以上综合问题称为TSP-CPP。为进一步提高规划效率,提出了一种自适应遗传算法,设计了自适应交叉算子和变异算子,随种群个体的适应度动态调整交叉概率和变异概率,保留精英个体;变异方式改为倒置变异,保持种群多样性,加快算法地收敛速度。在4种不同障碍物设置的仿真环境下进行了实验。结果表明,当环境复杂时,上述算法比动态规划法快约3000倍,比基于改进算子的遗传算法快约2倍。

基于改进DQN算法的应召搜潜无人水面艇路径规划方法

针对应召反潜中无人水面艇航向和航速机动的情形,提出一种基于改进深度Q学习(Deep Q-learning,DQN)算法的无人艇路径规划方法。结合应召搜潜模型,引入改进的深度强化学习(Improved-DQN,I-DQN)算法,通过联合调整无人水面艇(Unmanned Surface Vessel,USV)的动作空间、动作选择策略和奖励等,获取一条最优路径。算法采用时变动态贪婪策略,根据环境和神经网络的学习效果自适应调整USV动作选择,提高全局搜索能力并避免陷入局部最优解;结合USV所处的障碍物环境和当前位置设置分段非线性奖惩函数,保证不避碰的同时提升算法收敛速度;增加贝塞尔算法对路径平滑处理。仿真结果表明,在相同环境下新方法规划效果优于DQN算法、A^(*)算法和人工势场算法,具有更好的稳定性、收敛性和安全性。

基于FIA*-APF算法的蟹塘投饵船动态路径规划

为了提高无人投饵船在含障碍物河蟹养殖池塘自主巡航的作业效率和安全性,该研究提出基于改进A*算法与人工势场法相融合(fusion of improved A*and artificial potential field,FIA*-APF)的蟹塘投饵船动态路径规划算法。首先引入动态加权因子优化A*算法评价函数;其次加入转折惩罚函数并删除冗余点,接着利用B样条曲线对全局路径进行平滑处理;最后将改进A*算法得到的全局路径作为改进人工势场法中的引力路径,生成投饵船自主巡航高效路径。根据养殖池塘创建静态和动态2种仿真环境,分别对传统人工势场法(traditional artificial potential field,TAPF)、基于A*和人工势场法的融合算法(the A*and artificial potential field,TA*-APF)和FIA*-APF算法的性能进行20次测试。仿真试验结果表明:2种环境下,FIA*-APF算法的平均规划时间是TAPF算法的17.23%,是TA*-APF算法的51.96%,平均指令节点数量比TAPF算法减少50.64%,比TA*-APF算法减少65.03%,平均路径长度比TA*-APF算法减少2.82%。蟹塘试验结果表明:FIA*-APF算法的规划时间为TAPF算法的38.16%,为TA*-APF的62.42%,路径长度比TAPF算法减少29.13%,比TA*-APF减少10.15%;另外,TAPF和TA*-APF算法规划路径上大于60°的转角分别是FIA*-APF算法的3.28和2.62倍,大于100°的转角分别是FIA*-APF算法的3.73和1.67倍,该研究算法规划的路径更高效平滑。研究结果可为无人投饵船自主导航提供参考。

基于改进A^(*)蚁群融合算法的路径规划研究

随着智能化技术的发展,无人车路径规划技术在未来无人战场上将发挥重要的作用。针对A^(*)算法易发生碰撞障碍物的问题,提出通过改进转弯机制进行避碰。针对路径较长和不够平滑的问题,提出一种改进A^(*)蚁群融合算法。仿真结果表明,使用改进A^(*)蚁群融合算法得到的路径长度和平滑度更优,简单地图中路径长度减少2.34%,总转弯角度减小5.62%;复杂地图中路径长度减少2.62%,总转弯角度减小26.3%。因此,该算法在保证无人车避障的基础上,有利于其快速完成相应任务。

基于EMSDBO算法的无人机三维航迹规划

针对无人机(unmanned aerial vehicle,UAV)三维航迹规划问题,提出一种增强型多策略蜣螂算法的UAV航迹规划方法。首先,将飞行接近率和响应时间的动态约束添加到威胁成本代价中,并考虑UAV转弯性能的影响,建立三维任务空间模型与航迹代价函数。其次,在蜣螂算法中引入偏移估计策略、变螺旋搜索策略、准反向学习策略和逐维变异策略,提高算法的全局寻优能力和收敛速度。最后,给出了改进算法在三维环境下航迹规划的仿真结果。结果表明:综合考虑UAV机动性能和转弯性能,规划出的路径可以更加安全有效地避开危险源。相比其他算法,改进算法的寻优能力更好,规划的航迹质量更优。

融合简化可视图和A^(*)算法的矿用车辆全局路径规划算法

针对矿用车辆在狭窄、弯曲及有未知障碍物的井下巷道中的路径规划效率低的问题,提出了一种融合简化可视图(SVG)和A^(*)算法的全局路径规划算法DVGA^(*)。在构建真实环境点云地图基础上,连接车辆在不同视点下的可视切点,动态生成SVG;将可视切点依次存入OPEN表作为节点,根据A^(*)算法估价函数选取路径最短情况下的节点加入CLOSED表,得到最优路径点并存储路径,同时删除OPEN表中的其余节点,循环此过程,直到OPEN表中出现终点;最后利用路径平滑算法进一步减少路径节点数量,从而提高路径规划效率。实验结果表明,与完整可视图+A^(*)算法、SVG+A^(*)算法及SVGCA^(*)算法对比,DVGA^(*)算法对复杂长距离路径的规划时间最短,平均路径长度分别缩短了10.79%,6.26%和2.86%,具有更强的适应性和更高的规划成功率。井下试验结果表明:在巷道宽度变换区域和躲避静态障碍物时,相比SVGCA^(*)算法,DVGA^(*)算法规划的路径更加平滑;躲避动态障碍物时,DVGA^(*)算法能够及时进行路径纠正,保证了路径规划的时效性和稳定性;在复杂多变的巷道环境中,DVGA^(*)算法的规划时间和路径长度相比SVGCA^(*)算法分别减少了11.51%和1.54%,具有更高的环境适应性和稳定性。

基于改进哈里斯鹰算法的无人飞行器路径规划

针对无人飞行器三维路径规划问题,提出一种基于哈里斯鹰优化(Harris hawks optimization,HHO)算法的无人飞行器三维路径规划算法。首先根据路径规划代价指标和无人飞行器自身性能,建立路径规划模型确立代价函数和约束条件。接着针对传统HHO算法的不足,引入非线性能量因子来平衡全局搜索和局部搜索的关系,使算法避免陷入局部最小值;引入混沌映射对HHO算法进行初始化种群并对其进行局部混沌搜索,增强算法种群多样性和搜索能力。最后通过仿真实验证明,改进的哈里斯鹰优化(improvement Harris hawks optimization,IHHO)算法可以有效规划出安全的无人飞行器航线,并且能够跳出局部最小值和具备较优的收敛速度。

基于分区进化遗传算法的无人船自导航规划研究

为解决无人船中的自导航路径规划问题,先对无人船的任务进行分层研究,再对其中的路径规划层进行分析。在完成航行环境的栅格化处理后,采用分区进化遗传算法(PEGA)对航行路径进行适应度值选取和交叉变异处理,以选出最优航行路径。实验结果表明,PEGA算法具有稳定的寻优性能,在迭代至30代时达到稳定状态,且稳定适应度值为0.58,并且该算法还能通过不同的目标需求完成不同的最优路线规划。

基于人工势场算法和RRT算法的多无人机路径规划

为了解决在城市和山区复杂环境中的多无人机任务分配及路径规划问题,提出了一种基于人工势场算法和RRT融合算法的多无人机协同路径规划方法。基于人工势场算法基础优化斥力函数,加入机间斥力因子,实现了协同避撞。引入RRT算法进行拓展搜索,解决了无人机陷入局部极值点时单一人工势场算法目标不可达的问题。通过三维路径规划仿真实验和算法对比实验验证该方法的可行性,结果表明,融合路径规划算法可以在约束条件下找到全局最优路径。

生鲜农产品车辆和无人机组合配送路径优化及效果测试

【目的】为提升生鲜农产品的配送效率,提出生鲜农产品车辆与无人机组合配送路径优化方案。【方法】按照生鲜农产品冷链配送体系要求,采用模糊K-means聚类方法确定生鲜农产品配送中心;定义配送中心位置为车辆和无人机组合配送的起点,以生鲜农产品配送总成本最小为目标函数,利用蚁群算法和无人机物流车协调配送算法求解上述构建的目标函数,获取最低配送总成本的配送路径规划结果;引入安全启发函数优化蚂蚁转移规则,获取安全最高的无人机最优配送路径。再按照该方法,将生鲜农产品按照质量划分为重件和轻件,并计算重件和轻件在配送过程中产生的成本,获取在不同的总配送生鲜农产品快件数量下,优化前与优化后的配送成本。【结果】效果测试表明,该方法具有良好的聚类效果,簇类凝聚度(Davies-Bouldin, DB)指数值均低于0.15,可合理选取配送中心,保证配送中心的覆盖程度。并且通过对比该方法应用前后的数据对比可知,应用前重件和轻件的配送成本明显更高于应用后的成本。【结论】该方法可以更高质量地完成车辆和无人机组合配送路径规划,能够显著降低生鲜农产品的配送总体成本,应用效果满足应用需求,提升配送效率。

基于细菌觅食-改进蚁群优化算法的水面无人船路径规划

为了解决水面无人船全局路径规划问题,提出了一种细菌觅食-改进蚁群优化算法(bacterial foraging-improved ant colony optimization algorithm,BF-IACOA)。相较于传统蚁群优化算法(ant colony optimization algorithm,ACOA),该算法在路径搜索策略上考虑水面无人船航行需要尽可能减少转向次数和完全规避过大转向角的约束,引入转向角启发因子,综合求解转移概率;同时引入细菌觅食算法的繁殖操作和趋化操作,改进信息素浓度的更新方式,解决传统ACOA容易陷入局部最优解和收敛速度较慢的问题。仿真结果表明,相较于传统ACOA,BF-IACOA的全局搜索能力得到较大幅度的提升,并且收敛迭代次数减少超过30%;在实际水域环境模型下,BF-IACOA可以通过14次迭代为无人船规划出全局可行路径。