一种时间依赖路网最小时间路径规划算法研究

研究时间依赖路网(TDN)的最短路径规划算法,对指导人们出行和解决城市交通等问题具有十分重要的意义。在研究前人算法的基础上,提出了一种利用结构体数组来求解TDN路网最小时间路径规划算法。对算法的基本原理和结构体数组的构造进行了介绍,对算法实现流程及其中一些关键步骤进行了重点阐述,最后在VC++环境中利用MapX控件对算法进行了实验仿真。仿真结果表明,该算法具有较高的搜索效率,且能适应路况变化,基本满足现实需要。     

改进 Ｄ∗ｌｉｔｅ 和时间弹性带法的移动机器人路径规划

为解决D*lite算法在进行路径规划时搜索效率低,易受运动物体影响及路径贴近障碍物的问题,提出了一种结合改进D*lite算法和时间弹性带法的融合算法。首先,将改进的跳点搜索引入到D*lite算法中,让算法只对跳点进行访问,实现了规划时间和路径长度的双优化。之后,融合改进的时间弹性带法进行局部路径规划,提高算法动态规划能力的同时保证了规划路径的安全性。仿真结果表明,改进的D*lite算法比原始D*lite算法在路径长度、规划时长及路径节点数上分别平均优化了6.40％、49.13％和73.70％。在实际表现中,融合算法在动态情况下比原始D*lite算法可减少11.04％的路径长度、67.10％的规划时长及11.99％的运动时间,并且保证了机器人和障碍物间的最小距离。     

大规模AGV的改进时间窗路径规划

在机器人路径规划中,A*算法搜索路径时存在大量冗余节点,随着任务量增加,其搜索效率也会急剧下降,因此无法适应大规模任务下的路径规划。为此提出一种改进时间窗的有界次优A*算法用于求解大规模自动导引车（automatic guided vehicle,AGV）路径规划问题。算法使用时间启发式,并在搜索过程中采用时空搜索,规划无冲突的最优或次优路径。算法主要进行了三处改进：采用时间启发式,缩短了路径时间;采用动态时间窗算法,避免多次路径规划;优化了聚焦搜索算子,降低负反馈。通过MATLAB实验结果证明改进后的算法在进行多机器人路径规划时,能快速有效地规划出无冲突的平滑次优路径,搜索效率高,稳定性强。     

时间依赖有向无环网最小时间路径算法

经典模型及算法可解决固定弧权条件下的最短路问题,然而实际应用中孤权往往是动态的,即弧权依赖时间变化。本文提出一种特殊最短路径算法,即在有向无环网络中最小时间路径算法的一种实现。该算法是一种改进的扩散法,克服了扩散法的一些显著缺点。文中证明了该理论的正确性,最后列举了一个传统算法不能解决的实例,证明了该算算：法的正确性。     

基于基因算法的时间规正算法

本文提出了一种适用于孤立字识别的基于基因算法的时间规正算法;详细讨论了其中一些关键技术, 如编码方法、适应度技术、基因操作子设计等。该算法可弥补动态时间规划DTW的某些不足(1)使距离归一化因子M与实际路径相关, 这使不同路径的比较更合理(2)以自然方式提供多条最佳规划路径建立了试验数据库, 在试验结果的基础上提出了算法性能分析模型模板间距离遵循正态分布。通过与DTW及串行多路径搜索法的性能进行比较, 结果表明基因时间规正算法具有明显的识别优势。     

基于时间栅格法和免疫算法的机器人动态路径规

本文提出了一种机器人动态路径规划方法 ,该方法首先采用时间栅格法来标识动态障碍物 ,建立机器人的环境信息 ,然后使用免疫算法实现在动态环境下机器人的全局和局部路径规划 ,达到避障和避碰的目的。文中定义了免疫算法的多因素适应度函数由碰撞系数、距离、转角和安全系数决定。实验表明所提方法可以提出的高路径规划的效率 ,满足机器人实时导航要求。     

基于连接特性的路径规划算法及其在城市交通网络中的应用研究

提出了一种基于连接特性的路径规划算法，并针对城市交通网络的路径规划进行算法的验证和应用研究，该算法利用网络的连接特性，求取最少连接层数的路径作为较优的全局路径，这样获得的全局路径不是加权最少路径，为了提高算法精度，在权较大的两点之间插入新的结点，这样获得的路径是全局最优路径的可信度很高。这种算法的时间复杂度是线性的，即O（N),而且通过适当增大模型，可以控制路径规划的精度，并提出了控制精度的两个指数，经过对城市交通的具体例子的计算及分析，表明该方法快速，可靠及有效。     

基于栅格法的虚拟人快速路径规划

在栅格中使用经典的Dijkstra算法进行路径规划有计算量大,规划时间长、进行扩展判断的节点个数多等缺点.栅格的组织结构决定了栅格中最短路径的特性--组成最短路径的各线段间的最小夹角为90°.根据栅格及最短路径的特性,提出了一种在栅格中使用Dijkstra算法规划路径时减少扩展节点的个数,进而缩短规划时间、降低计算代价的算法,并将其用于虚拟人的路径规划.实验验证了算法的可行性和有效性.     

时间依赖型车辆路径问题的一种改进蚁群算法

时间依赖型车辆路径规划问题(TDVRP),是研究路段行程时间随出发时刻变化的路网环境下的车辆路径优化.传统车辆路径问题(VRP)已被证明是NP-hard问题,因此,考虑交通状况时变特征的TDVRP问题求解更为困难.本文设计了一种TDVRP问题的改进蚁群算法,采用基于最小成本的最邻近法(NNC算法)生成蚁群算法的初始可行解,通过局部搜索操作提高可行解的质量,采用最大--最小蚂蚁系统信息素更新策略.测试结果表明,与最邻近算法和遗传算法相比,改进蚁群算法具有更高的效率,能够得到更优的结果;对于大规模TDVRP问题,改进蚁群算法也表现出良好的性能,即使客户节点数量达到1000,算法的优化时间依然在可接受的范围内.     

时间最优的无人机巡检避障路径规划

无人机执行实际任务时，完成时间是衡量任务效能的重要指标，而路径规划方法一般以路径长度最小为目标，难以准确直接地反应完成任务时间。对此，在构建路径时间代价的近似计算方法基础上，本文提出一种最小化任务时间的无人机避障路径规划算法。通过路径跟踪特性试验，获取不同机动方式下的飞行时间，作为路径搜索过程中的代价计算依据。设计节点扩展方式和代价函数计算方法，提出一种改进A*搜索算法以实现障碍规避和路径时间优化。通过随机场景下的仿真试验对所提方法的有效性和性能优势进行验证。最后以变电站巡检为例，验证改进算法能够引导无人机以更短的时间完成避障巡检。     

多目标多时间窗车辆路径问题的鸽群-水滴算法

针对智能水滴算法求解带时间窗车辆路径规划收敛速度慢、计算精度差的问题,根据带时间窗车辆路径问题的应用要求,利用整数线性规划方法,以配送车辆的最小运输总成本、最短运输距离和最少安排数量为目标,综合考虑了车辆出发点、服务点、装载量、行驶距离、服务时间窗等诸多约束条件,构建了多目标多时间窗车辆路径模型;为了精准快速求解多目标多时间窗车辆路径模型,提出一种鸽群-智能水滴互补改进优化算法,将河道水滴离散二进制变换后,采用地图罗盘算子和地标算子分别改进水滴的流动速度和方向,并利用自适应变邻域扰动策略干扰水滴携带的泥土量,提高水滴算法的开发和探索能力;利用理想点法和罚函数与多目标优化混合方法分别处理多目标函数与约束条件,并以两种经典的带时间窗车辆路径问题为实例,通过与遗传算法、智能水滴算法和鸽群-水滴算法的计算结果进行比较,结果表明：在相同的算法参数和经济指标下,鸽群-水滴算法相比于智能水滴算法求解模型中的运输路径缩短20 km左右、运输成本节约403元左右,且该算法的求解时间和迭代次数也明显优于其他两种人工智能算法。     

时间依赖的网络中最小时间路径算法

时间依赖的网络与传统网络模型相比更具有现实意义，具有广泛的应用领域，交通网络和通信网络可以抽象为时间依赖的网络模型，当模型中弧的工度是时间依赖的变量，最短路径问题的求解变得非常困难，早期的研究者通过具体的网络实例认识到传统最短路径算法在这种情况下是不正确的，因此给出限制性条件使得传统最短路径算法是有效的。该文从最短路径算法的理论基础入手，从理论上证明了传统最短路径算法，如Dijkstra算法和标号设置算法，在时间依赖的网络上不能有效地求解最短路径问题，并且，在没有任何限制性条件下，给出了时间依赖的网络模型，理论基础，求解最小时间路径的优化条件和SPTDN算法，从理论上证明了SPTDN算法的正确性，算法的实验结果是正确的，最后给出了时间依赖的网络应用实例。     

A label-setting algorithm for finding a quickest path

The quickest path problem is to find a path to send a given amount of data from the source to the destination with minimum transmission time. To find the quickest path, existing algorithms enumerate non-dominated paths with distinct capacity, and then determine a quickest path by comparing their transmission time. In this paper, we propose a label-setting algorithm for finding a quickest path by transforming a network to another network where an important property holds that each subpath of a quickest path is also a quickest path. The proposed algorithm avoids enumerating non-dominated paths whose transmission time is greater than the minimum transmission time. Although the computational complexity of the proposed algorithm is the same as that of existing algorithms, experimental results show that our algorithm is efficient when a network has two or more non-dominated paths.     

Evolutionary multi-criteria trajectory modeling of industrial robots in the presence of obstacles

Optimal trajectory planning for robot manipulators is always a hot spot in research fields of robotics. This paper presents two new novel general methods for computing optimal motions of an industrial robot manipulator (STANFORD robot) in presence of obstacles. The problem has a multi-criterion character in which three objective functions, a maximum of 72 variables and 103 constraints are considered. The objective functions for optimal trajectory planning are minimum traveling time, minimum mechanical energy of the actuators and minimum penalty for obstacle avoidance. By far, there has been no planning algorithm designed to treat the objective functions simultaneously. When existing optimization algorithms of trajectory planning tackle the complex instances (obstacles environment), they have some notable drawbacks viz.: (1) they may fail to find the optimal path (or spend much time and memory storage to find one) and (2) they have limited capabilities when handling constraints. In order to overcome the above drawbacks, two evolutionary algorithms (Elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) algorithm) are used for the optimization. Two methods (normalized weighting objective functions method and average fitness factor method) are combinedly used to select best optimal solution from Pareto optimal front. Two multi-objective performance measures (solution spread measure and ratio of non-dominated individuals) are used to evaluate strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimizer overhead and algorithm effort are used to find computational effort of NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analyzed.     

改进人工势场法的移动机器人路径规划

传统人工势场法在路径规划过程中易陷入势场局部最小点和陷阱区域,面对较为复杂的障碍物环绕环境也难以规划出完整路径。针对这个问题,提出了一种改进人工势场法。引入机器人前进的方向向量,对斥力的生成和计算机制进行了调整以解决其处于局部最小点情况下无法继续规划路径的问题;添加了判断机制以识别周边环境状况,当机器人处于陷阱区域等复杂环境下时设立虚拟目标点以引导其向外运动从而摆脱陷阱区域。结果表明,改进算法可以有效解决传统算法容易出现的路径规划中断情况;同时与传统算法相比,其在随机障碍物环境中的规划路径长度减少,有效提高了路径规划效率。     

New polynomial time algorithms to compute a set of Pareto optimal paths for multi-objective shortest path problems

Multi-objective shortest path problem (MOSPP) is an active area of research because of its application in a large number of systems such as transportation systems, communication systems, power transmission systems, pipeline distribution systems of water, gas, blood and drainage, neural decision systems, production planning and project planning. In these networks it becomes necessary to find the best path from one node to a specified or all other nodes. The computational complexity of the existing algorithms in the literature to compute all Pareto minimum paths from a specified source node to all other nodes in an MOSPP is of exponential order in the worst case. Instead of generating all the values of the Pareto minimum paths in exponential time, we propose an algorithm to find a set of values of the Pareto minimum paths in polynomial time, which is very significant in many contexts. If an MOSPP of a network is having negative cycle, all the existing algorithm only indicate the existence of the negative cycle, that too after exponential number of operations. However, applying the proposed algorithm, we can find a set of Pareto minimum paths of any MOSPP of a network even if it contains negative cycles. The proposed algorithm is illustrated with examples.     

Minimum-time control of robotic manipulators with geometric path constraints

Conventionally, robot control algorithms are divided into two stages, namely, path or trajectory planning and path tracking (or path control). This division has been adopted mainly as a means of alleviating difficulties in dealing with complex, coupled manipulator dynamics. Trajectory planning usually determines the timing of manipulator position and velocity without considering its dynamics. Consequently, the simplicity obtained from the division comes at the expense of efficiency in utilizing robot's capabilities. To remove at least partially this inefficiency, this paper considers a solution to the problem of moving a manipulator in minimum time along a specified geometric path subject to input torque/force constraints. We first describe the manipulator dynamics using parametric functions which represent geometric path constraints to be honored for collision avoidance as well as task requirements. Second, constraints on input torques/ forces are converted to those on the parameters. Third, the minimum-time solution is deduced in an algorithm form using phase-plane techniques. Finally, numerical examples are presented to demonstrate utility of the trajectory planning method developed.     

两种求解机械手最短时间动作路径规划的新算法及其在基于PVR技术的控制平台上的实现

针对机械手在执行点到点的工作任务中所遇到的两类最短时间动作路径规划(MTMPP)问题, 分别提出了新的混合型进化计算模拟退火(EC SA)算法以及将EC SA算法与一些优化技术结合使用的EC SA-DP算法. 通过与目前较好的求解算法(如弹性网络方法ENM)以及其它一些近似优化算法(如遗传算法和模拟退火算法等)所进行的数值仿真比较, 验证了EC SA算法在处理复杂工作任务时的高效性. 这些算法在基于投射式虚拟现实(PVR)技术的控制平台上进行了虚拟仿真实现, 仿真结果表明可有效地提高虚拟环境中的投射式操作     

A fast two-stage ACO algorithm for robotic path planning

Ant colony optimization (ACO) algorithms are often used in robotic path planning; however, the algorithms have two inherent problems. On one hand, the distance elicitation function and transfer function are usually used to improve the ACO algorithms, whereas, the two indexes often fail to balance between algorithm efficiency and optimization effect; On the other hand, the algorithms are heavily affected by environmental complexity. Based on the scent pervasion principle, a fast two-stage ACO algorithm is proposed in this paper, which overcomes the inherent problems of traditional ACO algorithms. The basic idea is to split the heuristic search into two stages: preprocess stage and path planning stage. In the preprocess stage, the scent information is broadcasted to the whole map and then ants do path planning under the direction of scent information. The algorithm is tested in maps of various complexities and compared with different algorithms. The results show the good performance and convergence speed of the proposed algorithm, even the high grid resolution does not affect the quality of the path found.