基于视觉引导多AGV系统的改进A*路径规划算法

研究基于视觉引导自动引导车(AGV)的改进A*路径规划算法.首先,设计一种包含导航、定位和任务信息的图形编码标志方法,AGV通过识别位于车身前方网格型路径中有序排布的编码标志进行快速定位和下一位置预判,为多AGV规划奠定基础;其次,根据网格型路径构成的动态随机网络,提出一种改进A*算法,将AGV在运动时产生的动态时间耗...     

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.     

机器人路径规划中的改进型遗传算法

提出了一种应用于机器人路径规划的改进型遗传算法。针对机器人路径规划的实际应用,优化设计了交叉算子和变异算子,引入了自定义的插入和删除两种遗传操作。通过把地图特征信息作为参与决策的已知条件来约束遗传算子的操作过程,提高了算法的进化效率。自定义遗传算子的使用,使得算法对复杂地图也表现出良好的适应能力。计算机仿真实验证明该算法在最优解输出概率方面相对于基本遗传算法有了显著提高。     

基于改进遗传算法的机器人路径规划

标准遗传算法在解决各类优化问题中获得成功,但它在具体的应用中由于缺乏对特定知识的利用,其性能有待提高.针对机器人路径规划的实际应用,通过优化设计标准遗传算法中的交叉算子和变异算子,提出一种应用于机器人路径规划的改进型遗传算法.在把地图特征信息引入遗传算子的操作过程中提高了算法的进化效率.计算机仿真实验结果证明该算法在收敛速度、最优解输出概率方面相对于基本遗传算法有了显著提高.     

Online probabilistic learning for fuzzy inference system

Online learning is a key methodology for expert systems to gracefully cope with dynamic environments. In the context of neuro-fuzzy systems, research efforts have been directed toward developing online learning methods that can update both system structure and parameters on the fly. However, the current online learning approaches often rely on heuristic methods that lack a formal statistical basis and exhibit limited scalability in the face of large data stream. In light of these issues, we develop a new Sequential Probabilistic Learning for Adaptive Fuzzy Inference System (SPLAFIS) that synergizes the Bayesian Adaptive Resonance Theory (BART) and Rule-Wise Decoupled Extended Kalman Filter (RDEKF) to generate the rule base structure and refine its parameters, respectively. The marriage of the BART and RDEKF methods, both of which are built upon the maximum a posteriori (MAP) principle rooted in the Bayes’ rule, offers a comprehensive probabilistic treatment and an efficient way for online structural and parameter learning suitable for large, dynamic data stream. To manage the model complexity without sacrificing its predictive accuracy, SPLAFIS also includes a simple procedure to prune inconsequential rules that have little contribution over time. The predictive accuracy, structural simplicity, and scalability of the proposed model have been exemplified in empirical studies using chaotic time series, stock index, and large nonlinear regression datasets.     

应用于机器人路径规划的双向时效A*算法

针对时效A*算法为了大幅减少算法时间,导致路径规划长度增加和路径锯齿过多的问题,提出一种改进的双向时效A*算法,该方法将从起点和终点同时运行时效A*算法寻找路径,并采用多近邻栅格距离计算方案;同时,根据不同环境地图对传统A*算法、时效A*算法和双向时效A*算法运行结果进行对比研究及分析;最后,制定算法时间、路径长度两个指标来评判算法的优劣。实验结果显示,双向时效A*算法相对于传统A*算法,算法时间最大减少76.8%,相对于时效A*算法,时间最大减少55.4%,并解决了时效A*算法规划路径距离增加、路径不够平滑的问题。     

一种改进的机器人路径规划的蚁群算法

针对具有复杂回旋地形结构的机器人路径规划问题, 提出了一种改进的蚁群算法. 该算法引入自适应迁移概率函数实现蚁群具有正、反向运动的能力, 改善了算法的曲折迂回能力; 能见度信息中引入距离启发因素和障碍相交检测机制, 完成路径搜索与避障过程有机结合, 提高算法的搜索效率; 引入贪婪信息素更新策略和节点信息素分布, 降低了数据存储量, 改善了路径规划的效果和算法的收敛速度. 基于不同算法的比较仿真实验, 数值结果证实了该算法的有效性.     

改进A*算法的机器人全局最优路径规划

针对传统A^*算法规划的路径存在很多冗余点和拐点的问题,提出了一种基于A^*算法改进的高效路径规划算法。首先,改进评价函数的具体计算方式,减小算法搜索每个区间的计算量,从而降低寻路时间,并改变生成路径;其次,在改进评价函数具体计算方式的基础上,改进评价函数的权重比例,减少生成路径中的冗余点和拐点;最后,改进路径生成策略,删除生成路径中的无用点,从而提高路径的平滑性;此外,考虑到机器人的实际宽度,改进后算法引入障碍物扩展策略保证规划路径的可行性。将改进A^*算法与三种算法进行仿真对比,实验结果表明,改进后的A^*算法规划的路径更加合理,寻路时间更短,平滑性更高。     

基于改进A*算法的越野路径规划研究

针对车辆的越野路径规划问题, 研究并分析了地形坡度和地表属性对于车辆路径规划的综合影响。引入了窗口移动法对地形进行先期的坡度计算和通行性分析, 就轮式车辆和履带式车辆分别建立了地表属性的粗糙度评价指标, 并采用面积占优法将地表属性栅格化。通过建立禁忌表, 叠加了坡度与粗糙度的约束影响以减少搜索范围, 提高搜索效率。构造了改进A*算法的估价函数, 并结合expand表、open表、closed表以及path表设计了考虑坡度和粗糙度约束的路径优化算法。仿真结果表明, 该算法能够快速有效地实现符合真实地形环境的越野路径规划。     

一种基于改进Theta *的机器人路径规划算法

对Theta *算法进行改进,并用于解决机器人路径规划问题.首先,将障碍物对机器人产生的斥力作为一种惩罚函数加入到启发函数中,并合理地选择惩罚函数权重以确定启发函数.在此基础上,改进A *算法的变种——Theta *算法,提出对路径进行平滑处理的PS_Theta *算法.最后在二维仿真环境中进行验证及数据统计,并推广至三维复杂环境中,实验结果证明了算法的合理性与有效性     

一种基于改进RRT*的全局路径规划算法

针对传统RRT*全局路径规划算法在多障碍物复杂环境中搜索效率低、占用内存过大、搜索路径不平滑等问题,提出一种基于简化地图的区域采样RRT*算法(simplified map-based regional sampling RRT*,SMRS-RRT*).首先简化处理全局栅格地图,在此基础上寻找从起点到目标点的最优路径点...     

改进A*算法的移动机器人最短路径规划

针对复杂室内环境下移动机器人路径规划存在实时性差的问题,通过对Dijkstra算法、传统A^*算法以及一些改进的A^*算法的分析比较,提出了对A^*算法的进一步改进的思路。首先对当前节点及其父节点的估计路径代价进行指数衰减的方式加权,使得A^*算法在离目标点较远时能够很快地向目标点靠近,在距目标点较近时能够局部细致搜索保证目标点附近障碍物较多时目标可达;然后对生成的路径进行五次多项式平滑处理,使得路径进一步缩短且便于机器人控制。仿真结果表明,改进算法较传统A^*算法时间减少93.8%,路径长度缩短17.6%、无90°转折点,使得机器人可以连续不停顿地跟踪所规划路径到达目标。在不同的场景下,对所提算法进行验证,结果表明所提算法能够适应不同的环境且有很好的实时性。     

Optimization of layout and path planning of surgical robotic system

Positioning a surgical robot for optimal operation in a crowded operating room is a challenging task. In the robotic-assisted surgical procedures, the surgical robot’s end-effector must reach the patient’s anatomical targets because repositioning of the patient or surgical robot requires additional time and labor. This paper proposes an optimization algorithm to determine the best layout of the operating room, combined with kinematics criteria and optical constraints applied to the surgical assistant robot system. A new method is also developed for trajectory of robot’s end-effector for path planning of the robot motion. The average deviations obtained from repeatability tests for surgical robot’s layout optimization were 1.4 and 4.2 mm for x and y coordinates, respectively. The results of this study show that the proposed optimization method successfully solves the placement problem and path planning of surgical robotic system in operating room.

     

改进A*算法在机器人室内路径规划中的应用

传统A^*算法在面向机器人室内多U型障碍的特殊场景下规划路径时，容易忽略机器人实际大小，且计算时间较长。针对这个问题，提出一种改进A^*算法。首先引入邻域矩阵进行障碍搜索以提升路径安全性，然后研究不同类型和尺寸的邻域矩阵对算法性能的影响，最后结合角度信息和分区自适应距离信息对启发函数进行改进以提高计算效率。实验结果表明，改进A^*算法可以通过更改障碍搜索矩阵的尺寸来获得不同的安全间距，以保证不同机器人在不同地图环境下的安全性；而且在复杂大环境中与传统A^*算法相比寻路速度提高了28.07%，搜索范围缩小了66.55%，提高了机器人在遇到动态障碍时二次规划的灵敏性。     

Non-nominal path planning for robust robotic assembly

In manufacturing and assembly processes it is important, in terms of time and money, to verify the feasibility of the operations at the design stage and at early production planning. To achieve that, verification in a virtual environment is often performed by using methods such as path planning and simulation of dimensional variation. Lately, these areas have gained interest both in industry and academia, however, they are almost always treated as separate activities, leading to unnecessary tight tolerances and on-line adjustments.     

改进粒子群算法在机器人路径规划中的应用

针对粒子群优化算法易过早收敛而陷入局部最优的缺陷,结合移动机器人全局路径规划问题模型,提出一种带扰动机制的粒子群优化算法。对于进入进化停滞状态的个体,采用个体修正策略产生新个体将其替代,来引导算法搜索可行路径,帮助粒子逃离局部极值。仿真实验表明,与其他算法相比,该算法具有更好的搜索精度和全局寻优能力。     

基于节点优化的改进全局路径规划A*算法

目前越来越多的领域使用移动机器人代替人工工作。路径规划就是移动机器人正常工作的保障之一,A*算法就是一种路径规划算法。针对A*算法生成路径拐点多、路径较长的问题,提出了一种基于将搜索邻域扩大至5×5的随机数去除节点的改进A*算法。首先,将3×3的搜索邻域扩大至5×5,从而减少拐点个数,改善转折角度,去除冗余点;其次,引入一种随机数去除冗余节点的方法,该方法是通过随机连接节点判定其是否穿过障碍物来去除冗余节点,从而进一步去除A*算法路径列表的冗余点;最后,将改进的算法与A*算法在30×30的栅格地图中进行仿真比较,实验结果表明,改进的算法在多组路径中都有很好的优化效果,路径长度、运行时长和访问节点数分别平均减少了4.46%、24.83%和39.93%,从而有效改善A*算法生成拐点多、路径较长的问题。     

A path planning algorithm for industrial robots

Instead of using the tedious process of robot teaching, an off-line path planning algorithm has been developed for industrial robots to improve their accuracy and efficiency. Collision avoidance is the primary concept to achieve such goal. By use of the distance maps, the inspection of obstacle collision is completed and transformed to the configuration space in terms of the robot joint angles. On this configuration map, the relation between the obstacles and the robot arms is obvious. By checking the interference conditions, the collision points are indicated with marks and collected into the database. The path planning is obtained based on the assigned marked number of the passable region via wave expansion method. Depth-first search method is another approach to obtain minimum sequences to pass through. The proposed algorithm is experimented on a 6-DOF industrial robot. From the simulation results, not only the algorithm can achieve the goal of collision avoidance, but also save the manipulation steps.     

Biologically inspired node generation algorithm for path planning of hyper-redundant manipulators using probabilistic roadmap

This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.     

一种自动光学检测系统的多阶段路径规划算法

针对检测表面贴片产品缺陷的光学检测系统,提出一种三阶段的快速路径规划算法。该算法前两个阶段形成初步的检测路径,第三阶段利用顺序检测窗口的几何关系,迭代的调整检测窗口的中心位置,以减少检测路径长度和检测窗口数量。最后仿真实验证明了该算法是一种实用的快速的规划算法。