基于协同进化的多无人机航迹规划研究

关于多无人机航迹优化研究,针对复杂环境下多无人机(UAV)系统的航迹规划,达到摧毁目标最大化,解决不同无人机之间的协同和防撞问题,提出了一种利用合作型协同进化算法的多无人机三维航迹规划方法.利用数字地图建立了无人机安全飞行曲面,采用并行进化的方案,将每个无人机航迹规划当作一个子问题,通过协同函数和无人机间的防撞设计实现各无人机间的时间协同和空间防撞.各子种群采用自适应的进化方法,在保持多样性的同时,保证了算法收敛的快速性.仿真结果表明,算法有效实用,能快速得到各无人机的低空突防三维航迹,可为多无人机航迹优化提供手段.     

巡航导弹参考航迹规划算法研究

为了提高巡航导弹的生存能力和杀伤概率,预先规划参考飞行航迹是一个重要的方法.由程序生成规划用数字地图,根据遗传算法的基本思想,采用改进的编码机制对巡航导弹在威胁已知的模拟数字地图上的飞行航迹进行整体规划,航迹种群随机生成,用加入各种威胁和约束条件影响的适应值函数对个体进行评价,通过选择交叉变芹算了使种群进化,最后所得到适应值最高的航迹个体即可作为导掸的参考飞行航迹.通过编程仿真试验,所得参考航迹可以有效的实现地形跟踪、地形威胁回避.算法由于控制参数的限制,得到的最优路径是次优化的;遗传算法的初始种群及控制参数的确定足改善算法寻优性能的主要因素.     

基于M/N逻辑和一步延迟的航迹起始方法

研究基于M/N逻辑和基于一步延迟的航迹起始方法,结合两者提出一种新的航迹起始方法。对落入相关区域中的量测加强约束条件,使每一个测量只能在一条航迹中出现;将相邻2个周期的权值与阈值进行比较,减少杂波对起始性能的影响。仿真结果表明,该方法在保证低虚假航迹概率的情况下,能正确起始航迹,缩短航迹起始时间,适用于密集杂波环境下的多目标航迹起始。     

基于概率分析进化算法的飞行航迹规划

在分析传统进化算法解决飞行航迹规划问题缺点的基础上,提出了一种高阶概率分析进化算法-多变量贝叶斯优化算法,用于解决飞行器航迹规划问题。其主要特点是用多变量染色体构造贝叶斯网络,通过设计一个多变量K2评价方法评价得到的贝叶斯网络的优劣。仿真结果验证了算法的有效性。     

基于改进粒子群算法的UAV航迹规划方法

结合当前无人机集群发展趋势,针对航迹规划算法和策略问题开展研究,在分析经典粒子群算法和传统航迹规划方法基础上,提出了一种基于改进粒子群算法的航迹规划方法,将无人机航迹规划分为整体航迹规划和节点间航迹规划两部分,针对两部分对于搜索速度和解的精度的不同需求,结合环境模型及约束条件,分别设计粒子群航迹规划算法的评价函数;对于节点间粒子群航迹规划,通过设计分段式惯性权重调整公式改进粒子群算法,在保证了算法的搜索速度的同时,提高了航迹规划解的精度。通过仿真验证了该方法的正确性和可行性,横向对比其他算法策略分析了该方法的优越性。最后在算法自主实时性方向上对于后续的工作开展提出了期望。     

基于多种群遗传算法的水面无人艇航迹控制方法

针对水面无人艇(USV)的航迹控制问题,提出了一种由视线导向法和多种群遗传算法整定的PID航向控制器组成的航迹跟踪控制方法.该方法采用多种群遗传算法克服了传统遗传算法容易陷入局部最优的问题,增强了算法的全局寻优能力;并根据模型特点改进了适应度函数,使得对控制器性能的评价更加合理.与标准遗传算法和粒子群算法的对比仿真表明,多种群遗传算法在PID参数整定方面寻优能力更强、稳定性更高;同时,整定出的PID控制器针对不同的模型参数,均表现出收敛速度快、无超调、无稳态误差的优良特性.航迹仿真结果表明,设计的航迹控制方法能够有效跟踪给定航迹.     

基于遗传算法的飞行器参考航迹规划

针对飞行器航迹规划问题展开研究,为了规划出最优满意的飞行轨迹,分析了飞行器航迹规划中存在的威胁与自身约束条件,提出了一种关于遗传算法的航迹规划方案,采用改进编码机制对飞行器在已知威胁情况下飞行航迹进行整体规划。取航迹个体只包含一个染色体,每个染色体为一个航迹点序列,随机生成种群,通过选择交叉变异,并将各种威胁和约束条件的影响适当的加入到适应值函数中,得到优化路径进行仿真。仿真结果给出了不同加权比例下所得到的最优航迹,通过仿真验证了算法的有效性。     

改进量子遗传算法在无人机航迹规划中的应用

研究无人机航迹规划优化问题,有效地规避威胁,可提高无人机的生存能力.但传统量子遗传算法在航迹规划方面局部寻优精度较低、稳定性差.为解决上述问题,提出改进量子遗传算法的无人机航迹规划方法.首先上述算法采用一维编码表示航迹,并对影响有效规避威胁的适应度构造代价模型和惩罚策略;针对量子遗传算法初始种群的单一性,引入关于概率划分的小生境协同进化策略,并对各种群采用动态量子旋转角,并借鉴狼群分配原则对种群进行更新,提高收敛速度;利用精英选择运算,创建精华种群,保留父代中最佳个体.仿真结果表明,上述算法的无人机航迹规划效率高,稳定性好,能够获得平滑的低代价航迹,是一种有效可行的航迹规划算法,且具有一定的推广意义.     

应用OBDD和PSL的航迹规划方法研究

航迹规划是决定无人飞行器飞行航迹优劣的关键环节。由于无人飞行器飞行空域广,态势也较复杂,实际规划中常常面临搜索的状态多、收敛时间慢等问题,这成为无人飞行器执行飞行任务的瓶颈,解决的优化策略包括:缩小问题的状态空间以及根据问题的约束条件,在搜索中剪枝。模型检验的经典OBDD(有序二叉决策图)方法是表示状态和状态迁移的高效率的数据结构方法,可以简化状态系统的表示空间;而PSL是一种重要时序逻辑,利用PSL和一阶逻辑描述无人飞行器航迹规划的领域约束,以期在规划中剪枝搜索状态。在使用上述两种优化策略基础上设计了航迹规划搜索算法,并实现了该算法的规划仿真,仿真结果表明该方法是一种有效可行的航迹规划方法。     

燃油约束下航迹规划算法研究与仿真

现有航迹规划算法通常不能够综合路径规划过程中的多种约束因素,且很少考虑到推进系统的能力限制,致使规划出的航迹实际不可飞。针对该问题,提出了一种满足飞行器多种机动性约束条件的航迹规划算法。对飞行器在垂直面内的运动状态进行分析,在传统代价函数的基础上提出了以燃油消耗为优化目标的代价函数。仿真结果表明,改进的代价函数能够对航迹进行很好的评价,所设计的规划算法搜索效率高,规划出的航迹实际可飞。     

Case-based path planning for autonomous underwater vehicles

Case-based reasoning is reasoning based on specific instances of past experience. A new solution is generated by retrieving and adapting an old one which approximately matches the current situation. In this paper, we outline a case-based reasoning scheme for path planning in autonomous underwater vehicle (AUV) missions. An annotated map database is employed to model the navigational environment. Routes which are used in earlier missions are represented as objects in the map. When a new route is to be planned, the path planner retrieves a matching route from the database and modifies it to suit to the current situation. Whenever a matching route is not available, a new route is synthesized based on past cases that describe similar navigational environments. Case-based approach is thus used not only to adapt old routes but also to synthesize new ones. Since the proposed scheme is centered around reuse of old routes, it would be fast especially when long routes need to be generated. Moreover, better reliability of paths can be expected as they are adapted from earlier missions. The scheme is novel and appropriate for AUV mission scenarios. In this paper, we describe the representation of navigation environment including past routes and objects in the navigational space. Further, we discuss the retrieval and repair strategies and the scheme for synthesizing new routes. Sample results of both synthesis and reuse of routes and system performance analysis are also presented. One major advantage of this system is the facility to enrich the map database with new routes as they are generated.This work was supported in part by National Science Foundation Grant No. BCS-9017990.     

Evolutionary Route Planner for Unmanned Air Vehicles

Based on evolutionary computation, a novel real-time route planner for unmanned air vehicles is presented. In the evolutionary route planner, the individual candidates are evaluated with respect to the workspace so that the computation of the configuration space is not required. The planner incorporates domain-specific knowledge, can handle unforeseeable changes of the environment, and take into account different kinds of mission constraints such as minimum route leg length and flying altitude, maximum turning angle, and fixed approach vector to goal position. Furthermore, the novel planner can be used to plan routes both for a single vehicle and for multiple ones. With Digital Terrain Elevation Data, the resultant routes can increase the surviving probability of the vehicles using the terrain masking effect.     

An adaptive guidance approach for the heuristic solution of a minimum multiple trip vehicle routing problem

One of the most important problems in combinatorial optimization is the well-known vehicle routing problem (VRP), which calls for the determination of the optimal routes to be performed by a fleet of vehicles to serve a given set of customers. Recently, there has been an increasing interest towards extensions of VRP arising from real-world applications. In this paper we consider a variant in which time windows for service at the customers are given, and vehicles may perform more than one route within a working shift. We call the resulting problem the minimum multiple trip VRP (MMTVRP), where a “multiple trip” is a sequence of routes corresponding to a working shift for a vehicle. The problem objective is to minimize the overall number of the multiple trips (hence the size of the required fleet), breaking ties in favor of the minimum routing cost.     

A template-based Tabu Search algorithm for the Consistent Vehicle Routing Problem

This paper presents a generic template-based solution framework and its application to the so-called Consistent Vehicle Routing Problem (ConVRP). The ConVRP is an NP-hard combinatorial optimization problem and involves the design of a set of minimum cost vehicle routes to service a set of customers with known demands over multiple days. Customers may receive service either once or with a predefined frequency; however frequent customers must receive consistent service, i.e., must be visited by the same driver over approximately the same time throughout the planning period. The proposed solution framework adopts a two-level master-slave decomposition scheme. Initially, a master template route schedule is constructed in an effort to determine the service sequence and assignment of frequent customers to vehicles. On return, the master template is used as the basis to design the actual vehicle routes and service schedules for both frequent and non-frequent customers over multiple days. To this end, a Tabu Search improvement method is employed that operates on a dual mode basis and modifies both the template routes and the actual daily schedules in a sequential fashion. Computational experiments on benchmark data sets illustrate the competitiveness of the proposed approach compared to existing results.     

Deadlock-free scheduling and control of flexible manufacturing cells using automata theory

This paper presents a novel method for the scheduling and control of flexible manufacturing cells (FMCs). The approach employs automata, augmented by time labels proposed herein, for the modeling of machines, transportation devices, buffers, precedence constraints, and part routes. Ramadge-Wonham's supervisory-control theory is then used to synthesize a deadlock-free controller that is also capable of keeping track of time. For a given set of parts to be processed by the cell, A/sup */ search algorithm is subsequently employed using a proposed heuristic function. Three different production configurations are considered: Case 1) each part has a unique route; Case 2) parts may have multiple routes, but same devices in each route; and Case 3) parts may have multiple routes with different devices. The proposed approach yields optimal deadlock-free schedules for the first two cases. For Case 3, our simulations have yielded effective solutions but in practice, optimal deadlock-free schedules may not be obtainable without sacrificing computational time efficiency. One such nontime-efficient method is included in this paper. The proposed approach is illustrated through three typical manufacturing-cell simulation examples; the first adopted from a Petri-net-based scheduling paper, the second adopted from a mathematical-programming-based scheduling paper, and the third, a new example that deals with a more complex FMC scenario where parts have multiple routes for their production. These and other simulations clearly demonstrate the effectiveness of the proposed automata-based scheduling methodology.     

A hybrid method based on linear programming and tabu search for routing of logging trucks

In this paper, we consider an operational routing problem to decide the daily routes of logging trucks in forestry. This industrial problem is difficult and includes aspects such as pickup and delivery with split pickups, multiple products, time windows, several time periods, multiple depots, driver changes and a heterogeneous truck fleet. In addition, the problem size is large and the solution time limited. We describe a two-phase solution approach which transforms the problem into a standard vehicle routing problem with time windows. In the first phase, we solve an LP problem in order to find a destination of flow from supply points to demand points. Based on this solution, we create transport nodes which each defines the origin(s) and destination for a full truckload. In phase two, we make use of a standard tabu search method to combine these transport nodes, which can be considered to be customers in vehicle routing problems, into actual routes. The tabu search method is extended to consider some new features. The solution approach is tested on a set of industrial cases from major forest companies in Sweden.     

An Alternative Approach for Solving the Environmentally-Oriented Discrete Network Design Problem

This paper addresses the discrete network design problem (DNDP) with emphasis on the environmental benefits. These benefits are traditionally quantified by emission models, which in general account for vehicle speeds, traffic flows and emission coefficients. An alternative approach for approximating the environmental impact of traffic is developed. This approach finds the route that keeps the most balanced speed profile throughout the route, which contributes to fuel consumption reduction. The paper formulates an optimization problem that includes the described approach for the DNDP. The solution of the problem consists of projects that contribute the most to the generation of such “balanced speed routes”. The paper illustrates the problem and the solution for a real-size network with a medium-size set of candidate projects.     

A computerised vehicle routing system for refuse collection

This paper describes the development and structure of a computerised vehicle routing system for refuse collection in Singapore. The system uses the ‘route first-cluster second’ approach with some enhancement made in order to improve the CPU time requirement. The results obtained with data from some existing routes are also described.     

航迹规划的一种路线图方法

该文提出一种飞行路线图上的实时三维航迹规划方法,将航迹规划过程分成两个阶段:学习阶段和查询阶段。在学习阶段,环境信息结合在路线图中,在查询阶段,采用SAS算法搜索飞行路线图,实时获得三维可行航迹。构图和航迹搜索过程中分阶段满足飞行器约束条件。通过更新路线图中边的代价,实现了动态环境中飞行器的实时规划。     

The design of an IoT-based route optimization system: A smart product-service system (SPSS) approach

The aim of route optimization system (ROS) is to design a set of vehicle routes to fulfill transportation demands, in an attempt to minimize cost and/or other negative social and environmental impacts. ROS, established based on the fruitful studies of vehicle routing problem (VRP), has been applied in various industries and forms. During daily operations, dynamic traffic conditions, varying restriction policies, road constructions, drivers’ progressing familiarity with the routes and destinations are all common factors affecting the performance of ROS. However, most current systems are designed in a one-way and open-loop manner, i.e. these systems do not track how the planned vehicle routes are performed, which hinders the continuous improvement of the system and would lead to the failure of the system. This study proposes a smart product-service system (SPSS) approach to design an IoT-based ROS, arguing that the product (i.e. the ROS) and services (updating base data and learning users’ behaviors automatically to optimize the system) should be designed as a bundle. For this end, IoT devices are employed to acquire real-time information and feedbacks of vehicles and drivers, which are used to assess the execution of planned routes and dynamically modify the base data. Moreover, the driving records from IoT devices reveal drivers’ improving familiarity with routes and destinations, which will be considered to optimize the assignment of routes to drivers. Finally, we use a case of retailing industry to show the advantages of the proposed SPSS approach.