机器人避障问题的最优设计探索

对静态机器人避障问题进行了全面分析,对最短路的设计进行了理论分析和证明,建立了机器人避障最短路径的几何模型,对最短时间路径问题通过建立非线性规划模型,有效地解决了转弯半径、圆弧圆心位置和行走时间等问题.     

机器人路径规划算法及其应用

本文研究环境已知条件下的移动机器人路径规划问题,提出了一种基于人工神经网络的路径规划算法,所提算法可以规划出折线型的最短路径,并且计算简单,收敛速度快。将所提算法应用于机器人“Khepera“,通过模拟实验,表明所提算法有效。     

基于改进蚁群算法的最短路径问题研究及应用

蚁群系统作为一种蚁群算法是解决最短路径问题的一种行之有效的方法.然而,它自身也存在着一些缺陷,主要针对基本蚁群算法易陷入局部最优这一缺陷对其进行改进,集中体现在初始信息素求解和信息素更新这两方面.为了进一步了解改进蚁群算法的优点,进行了实验仿真:将改进的蚁群算法应用子模拟医疗救护GIS中,利用GIS的网络分析功能对城市道路网络的最短路径选择算法进行了深入地探讨研究,并以山西省太原市的交通路线作为实例进行研究.计算机仿真结果表明,改进的蚁群算法在解决最短路径问题时较基本蚁群算法的性能好,它具有一定的理论参考价值和现实意义.     

用嵌套插队算法解决TSP问题

本提出了一种求解TSP问题的近似算法—嵌套插队算法。这种算法结合了启发式算法和随机化算法以及局部寻优的思想。实验结果表明对于较小规模的。TSP问题，直接用插队算法(QJA)就能以很大的概率获得已知最优解。对于规模较大的问题实例。嵌套插队算法(NQJA)能获得质量高于名的启发式算法的解。另外，用嵌套插队算法找到的China144的最短路径优于目前已知的最短路径。嵌套插队算法是专门针对TSP问题而提出的，但其思想也可以给求解其他NP难解的组合优化问题以启发。     

突发事件中对嫌疑犯有效封堵的模型与算法

基于CUMCM-2011 B题中关于嫌疑犯的封堵问题的研究.通过建立描述市区交通网络图的权矩阵,采用求最短路的Dijstra算法求出市区任意两节点的最短路径及路长,构作最佳路径阵和距离矩阵,以此为基点建立封堵路口的最优调度方案模型,再在此基础上建立封堵住嫌疑犯的最优模型,并设计了模型求解的算法.将算法应用于CUMCM-2011 B题中关于嫌疑犯的封堵问题,获得最优封堵方案.     

多无人机协同任务规划问题

研究无人机任务规划问题,从无人机侦查和轰炸两方面入手.首先,运用迭代算法求解出从基地到雷达区域边际上任一目标出入口的最短路径.在此基础上,以无人机在雷达范围内滞留时间最短,以及被探测次数最少为目标,建立多目标最优化模型.通过改进交叉算子的遗传算法找出最优侦查路径.对于轰炸任务,以无人机在雷达范围内滞留时间最短,以及轰炸总时间最短为目标,建立多目标最优化模型.采用改变惯性权重的自适应粒子群算法找出最佳轰炸路线.由于计算时间较长,本文对68个目标进行聚类分析,提出针对轰炸任务的快速算法,相较原轰炸方案,其计算效率提高80%以上.     

复杂地势的无人机灾情巡查新方法

主要针对无人机在抢险救灾中的灾情巡查问题探究新型巡查方法.通过变"覆盖巡查面"为"有效巡查点",将传统的多无人机协同覆盖巡查问题转化为带有避障的VRP问题,并以所有无人机总飞行时间最少为目标建立相应的数学模型.运用MATLAB编写蚁群算法求解VRP中任意两点间的最短避障路线长度矩阵,进而用遗传算法来求解带有避障的VRP问题,得到不同需求下的最少无人机数量,规划出飞行路线.经过案例分析可得,此巡查路线覆盖率达到85.95%,具有较好的实用性.     

最小权点覆盖问题的一个近似算法

在点、边赋权的简单图中,关于最小权点覆盖问题,以经典的最短路算法-Dijkstra算法为基础,提出了一个求解该问题的近似算法.首先,在给定的赋权图中任选一点作为初始点,并给出允许集及相关定义.然后,利用经典的最短路算法-Dijkstra算法,求出初始点到允许集中各顶点的最短路径,并按照一定的原则选择近似最小权点覆盖集.最后,通过算例阐释了算法的实现过程的合理性及有效性.     

Bellman最优原理在复杂加权网络中的应用研究

针对现有算法及软件计算复杂加权网络介数的局限性,应用Bellman最优原理于复杂加权网络介数计算中,并针对复杂网络动态演化,节点众多,重点,节点间无边连接等特点作了相应修改.依算法实例计算出了复杂加权网络的最短路径长、最短路径和介数,最后经验证算法具有较快的运行速度和较准确的结果.     

圆台上蚂蚁爬行最短路径问题

蚂蚁爬行的最短路径问题,是讨论在规则立体图形表面上蚂蚁从一点爬到另外一点如何选择路径所走路程最短的问题.此问题背景简单、生动、活泼,而解决此问题中需要运用几何学中两点之间线段最短等基础知识,并渗透了把空间问题转化为平面问题的等基本数学思想方法.对于蚂蚁在立方体、长方体、圆柱、圆锥、圆台表面爬行的最短路径问题,在文[1]、[2]中都进行了一些讨论.同时也有很多老师利用此背景进行了很多行之有效的教学设计.     

Bacterial memetic algorithm for offline path planning of mobile robots

The goal of the path planning problem is to determine an optimal collision-free path between a start and a target point for a mobile robot in an environment surrounded by obstacles. This problem belongs to the group of combinatorial optimization problems which are approached by modern optimization techniques such as evolutionary algorithms. In this paper the bacterial memetic algorithm is proposed for path planning of a mobile robot. The objective is to minimize the path length and the number of turns without colliding with an obstacle. The representation used in the paper fits well to the algorithm. Memetic algorithms combine evolutionary algorithms with local search heuristics in order to speed up the evolutionary process. The bacterial memetic algorithm applies the bacterial operators instead of the genetic algorithm??s crossover and mutation operator. One advantage of these operators is that they easily can handle individuals with different length. The method is able to generate a collision-free path for the robot even in complicated search spaces. The proposed algorithm is tested in real environment.     

Navigation of underwater robot based on dynamically adaptive harmony search algorithm

The current research work has employed an evolutionary based novel navigational strategy to trace the collision free near optimal path for underwater robot in a three-dimensional scenario. The population based harmony search algorithm has been dynamically adapted and used to search next global best pose for underwater robot while obstacle is identified near about robot’s current pose. Each pose is evaluated based on their respective value for objective function which incorporates features of path length minimization as well as obstacle avoidance. Dynamic adaptation of control parameters and new perturbation schemes for solution vectors of harmony search has been proposed to strengthen both exploitation and randomization ability of present search process in a balanced manner. Such adaptive tuning process has found to be more effective for avoiding early convergence during underwater motion in comparison with performances of other popular variants of Harmony Search. The proposed path planning method has also shown better navigational performance in comparison with improved version of ant colony optimization and heuristic potential field method for avoiding static obstacles of different shape and sizes during underwater motion. Simulation studies and corresponding experimental verification for three-dimensional navigation are performed to check the accuracy, robustness and efficiency of proposed dynamically adaptive harmony search algorithm.     

“货到人”拣选系统机器人任务分配的鲁棒双层规划模型

在电商“货到人”拣选系统中,如何调度系统中的机器人并对任务进行合理地分配决定着整个系统的运行效率与成本。分析“货到人”拣选系统作业流程,建立机器人数量配置、机器人调度与机器人任务分配的双层规划模型。上层模型以批量订单完成总成本最小为目标函数,以机器人调度为决策变量,构建整数规划模型;下层模型以机器人完成所有任务的平均空闲率最小为目标函数,以任务分配为决策变量,考虑机器人在完成任务过程中由于调度、避障、路径规划等导致的行走距离不确定因素,构建鲁棒优化模型。上层的调度结果制约了下层的最小平均空闲率,下层的任务分配结果影响上层的最小成本,上下层结果共同决定机器人配置决策。利用遗传算法求解模型,通过实例仿真验证了模型的有效性。     

Generating optimal path by level set approach for a mobile robot moving in static/dynamic environments

According to the analogy between the mobile robot navigation path and the heat transferring path under steady state, the robot path planning problem during navigation is converted into identify the heat transferring path that minimizes the thermal compliance across the analysis domain. A new path planning approach which combines the concept of growth simulation and level set based heat conduction topology optimization framework is adopted to determine the heat transferring path. By introducing the concept of growth simulation, the proposed approach could calculate a few steps of the navigation path, which is of great significance for online reactive navigation. The proposed approach could avoid local minima and search for the optimal growth orientation freely without constraints from background mesh since the inherent characteristics of heat conduction and the level set approach, respectively. A new reactive navigation algorithm based on the proposed path planning approach and the concept of temporarily safe path is proposed to navigate the mobile robot from the start point to the goal point in unknown dynamic environment with static and dynamic obstacles. Diverse simulation cases are carried to illustrate the effectiveness of the reactive navigation algorithm.     

多无人机协同任务策略优化

从研究多无人机协同任务的系统资源分配、任务分配、航线规划、轨迹优化等问题入手,建立了多基地多无人机协同侦察模型.针对问题,首先利用"栅格化聚拢"的思想对目标点进行过滤优化,进而对目标群和无人机基地进行了任务分配,而后结合蚁群算法、贪心算法、最短路径算法等思想,通过Matlab平台,计算出能够让无人机停留在雷达探测区域总时间最少的最优策略.     

A shortest path algorithm in robotics and its implementation on the FPS T-20 hypercube

A broad class of problems involving the optimal control of robot arms can be formulated as dynamic programming problems whose structure is particularly attractive for parallel processing. For certain simple cost functions the dynamic programming formulation reduces to determining the shortest path through a network. This algorithm has been implemented on a Floating Point Systems' T-20 hypercube computer. An analysis of the performance of the algorithm provides several important insights into the interplay between problem size and the number of processors in a parallel computer. The results also underscore the potential for parallel computers in real-time control applications.This work was supported in part by the Office of Naval Research, Contract N 00014-86-K-0693.     

An evidential approach to SLAM,path planning,and active exploration

Probability theory has become the standard framework in the field of mobile robotics because of the inherent uncertainty associated with sensing and acting. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages over probabilistic approaches in the context of robotics. We do so by presenting solutions to the essential problems of simultaneous localization and mapping (SLAM) and planning based on belief functions. For SLAM, we show how the joint belief function over the map and the robot's poses can be factored and efficiently approximated using a Rao-Blackwellized particle filter, resulting in a generalization of the popular probabilistic FastSLAM algorithm. Our SLAM algorithm produces occupancy grid maps where belief functions explicitly represent additional information about missing and conflicting measurements compared to probabilistic grid maps. The basis for this SLAM algorithm are forward and inverse sensor models, and we present general evidential models for range sensors like sonar and laser scanners. Using the generated evidential grid maps, we show how optimal decisions can be made for path planning and active exploration. To demonstrate the effectiveness of our evidential approach, we apply it to two real-world datasets where a mobile robot has to explore unknown environments and solve different planning problems. Finally, we provide a quantitative evaluation and show that the evidential approach outperforms a probabilistic one both in terms of map quality and navigation performance.     

A heuristic approach to hard constrained shortest path problems

Constrained shortest path problems have many applications in areas like network routing, investments planning and project evaluation as well as in some classical combinatorial problems with high duality gaps where even obtaining feasible solutions is a difficult task in general.We present in this paper a systematic method for obtaining good feasible solutions to hard (doubly constrained) shortest path problems. The algorithm is based essentially on the concept of efficient solutions which can be obtained via parametric shortest path calculations. The computational results obtained show that the approach proposed here leads to optimal or very good near optimal solutions for all the problems studied.From a theoretical point of view, the most important contribution of the paper is the statement of a pseudopolynomial algorithm for generating the efficient solutions and, more generally, for solving the parametric shortest path problem.     

Location of an Obnoxious Route

This problem deals with the location of a route or path through a set of given points in order to maximize the smallest weighted distance from the given points to the route. Applications may include the planning of pipelines carrying noxious material, and also certain problems in robotics. The first algorithm finds a non-linear path by iteratively solving network minimal-cut problems. A second algorithm solves the case where the route is restricted to be linear.