不确定动态环境下移动机器人的完全遍历路径规划

基于生物激励神经网络、滚动窗口和启发式搜索,提出了一种新的完全遍历路径规划方法．该方法用Grossberg的生物神经网络实现移动机器人的局部环境建模,将滚动窗口的概念引入到局部路径规划,由启发式算法决定滚动窗口内的局域路径规划目标．该方法能在不确定动态环境中有效地实现机器人自主避障的完全遍历路径规划．仿真研究证明了该方法的可用性和有效性．     

基于改进蚁群算法的全向移动机器人全遍历路径规划

受全遍历环境影响, 现有方法规划得出的路径长度过长, 为提高路径规划性能, 获取最优路径, 提出基于改进蚁群算法的全向移动机器人全遍历路径规划方法. 在拓扑建模示意图的基础上, 依据移动机器人在原坐标系下的位置信息, 利用角度转换建立新的环境模型. 考虑蚁群算法存在的问题, 将递减系数引入到启发函数中, 更新局部信息素, 通过设定迭代阈值, 调节信息素的挥发系数. 最后通过路径规划流程设计, 实现对全向移动机器人全遍历路径的规划. 实验结果表明, 所设计方法不仅可以缩短全遍历路径长度, 还可以缩短路径规划时间, 获取最优路径, 从而提高了全向移动机器人的全遍历路径规划性能.     

移动机器人的完全遍历路径规划:生物激励与启发式模板方法

提出了基于生物激励神经网络的一种新的完全遍历路径规划方法.该方法集成了模板模型、启发式搜索和障碍物逼近算法.一种称为分流合作-竞争反馈网络的生物激励神经网络被用于移动机器人的工作环境建模,而模板模型法、启发式算法和障碍物逼近算法用于移动机器人的避障路径规划,其中障碍物逼近算法能够实现不规则形状障碍物周边区域的遍历,以进一步改善路径规划的覆盖区域.仿真研究表明,本文方法使得路径规划的性能得到明显的改进,例如规划路径的重叠率,而且算法简单有效.     

完全多叉树的叶子节点构造搜索模型的算法与应用

设计了完全多叉树的存储结构和遍历算法,并设计了根据子节点搜索整个完全多叉树算法,最后给出了一个实际的完全三叉树的应用例子。     

完全多叉树的叶子节点构造搜索模型的算法与应用

设计了完全多叉树的存储结构和遍历算法,并设计了根据子节点搜索整个完全多叉树算法,最后给出了一个实际的完全三叉树的应用例子。     

基于非理想SIC的STAR-RIS辅助NOMA系统性能研究

将可同时透射和反射的可重构智能表面(Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface, STAR-RIS)与非正交多址接入(Non-Orthogonal Multiple Access, NOMA)技术结合起来,研究了系统的遍历速率与中断概率。考虑到实际中NOMA用户很难获得完全串行干扰消除(Successive Interference Cancellation, SIC),研究了STAR-RIS辅助NOMA系统有着不完全SIC时的下行链路性能。首先通过对NOMA用户解码的信干噪比进行分析,得到其概率密度函数。然后基于用户信干噪比的概率密度函数推导了用户的遍历速率,并分析了高发射功率下用户速率的渐近性能。最后分析了系统中断性能,并推导了系统中断概率的近似闭式表达式。仿真结果验证了理论分析的有效性,即所推导的理论遍历速率和中断概率可与相应的仿真取得较好的一致,由于不完全SIC的存在,系统性能要差于完全SIC下的性能。此外还分析了STAR-RIS元素数量的不同对于系统性能的影响,发现元素...     

基于A*算法的停车场路径引导设计

为提高停车场停车效率和选择最佳停车位,根据停车场的车位布局及车位使用状况,设计了泊车路径规划模型。把空闲车位抽象为二维坐标系的坐标点,分别计算其距离用户位置的曼哈顿距离,选取曼哈顿距离最小值的停车位为最优车位。选用改进的A*算法为停车路径规划算法,为进一步优化算法遍历过程,使之更加适用于停车场车位分布较分散的情况,以停车位对应路段为遍历节点进行路径搜索,并完成路径规划,结果表明:该方法有效地去除了冗余节点,提高了算法遍历速度。     

一种非完全图最短遍历路径的新型求解方法

本文提出一种通过非完全图虚拟化完全图,并利用遗传算法求解虚拟完全图的最短 遍历路径,最终解决非完全国最短遍历路径问题的新方法,实验证明：该方法能有效 求解非完全图最短遍历路径问题,为工程应用提供了一种强有力的数学工具。     

基于欧拉图的授权扩散拓扑构建与授权撤销

在分散式自主授权模式中,接受授权的用户可以将转授给他的权限再次转授给其他人,经过多步转授的权限扩散与不完全的委托撤销可能导致隐性授权冲突.在以往的授权模型中,模型设计的重点在于如何授权,而对于授权撤销考虑甚少.由于转授权路径生成的随意性,增加了遍历路径完成授权回收的难度.针对授权路径的生成和转授权回收进行研究,引入欧拉图对授权路径构建进行约束,在此基础上给出了授权路径构建算法与转授权路径遍历回收方法,通过有目的的授权路径构建,简化转授权路径遍历过程,解决转授权路径遍历不完全导致的授权撤销不完整问题,防止权限扩散并消除隐性授权冲突.     

硬件加速的数字化放射图像重建算法

数字化放射图像重建技术被广泛应用于癌症放射治疗中,包括诊断、术前规划及术内辅助．提出一种两步法硬件加速数字化放射图像重建算法：第一步将光线投射的三个步骤,即几何变换、场景遍历和渲染,完全在可编程图形硬件中实现,从而快速模拟了X-光线的衰减过程;第二步利用浮点精度纹理和render-to-texture功能,快速模拟出高质量的X-光线的负片曝光效果．实验结果表明,该算法能高质量地交互重建数字化放射图像,适用于放射诊断及治疗等应用．     

A path planning and path‐following control framework for a general 2‐trailer with a car‐like tractor

Maneuvering a general 2‐trailer with a car‐like tractor in backward motion is a task that requires a significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path‐following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice‐based path planning framework is developed in order to generate kinematically feasible and collision‐free paths and a path‐following controller is designed to stabilize the lateral and angular path‐following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed, which only utilizes information from sensors that are mounted on the car‐like tractor, making the system independent of additional trailer sensors. The proposed path‐planning and path‐following control framework is implemented on a full‐scale test vehicle and results from simulations and real‐world experiments are presented.     

Polynomial joint angle arm robot motion planning in complex geometrical obstacles

This paper addresses a point-to-point of an arm robot motion planning in complex geometrical obstacle. It will govern a two-layer optimization strategy utilizing sixth degree polynomial as joint angle path. At the beginning of the motion planning process, the path planning starts with the optimization objective to minimize the joint angle travelling distance under collision detection rules as constraint. After the best path has been met, the associated time will be searched with the optimization objective to minimize the total travelling time and the torque under the maximum velocity, the maximum acceleration, the maximum jerk, and the maximum torque constraints. The performance of a Genetic Algorithm (GA) and a Particle Swarm Optimization (PSO) will be investigated in searching the feasible sixth degree polynomial joint angle path and the total travelling time that gives the optimal trajectories under kinodynamic constraints. A 3-Degree-Of-Freedom (3-DOF) planar robot will be utilized to simulate the proposed scenario.     

Arm path planning of a space robot with angular momentum

Arm path planning of a space robot with angular momentum is considered in this paper. A space robot changes its attitude by the arm motion and angular momentum of the space robot has the possibility to reduce the attitude change. A path planning method of the arm where the final satellite attitude becomes the same as the initial one is proposed. The method derives an approximate path first based on the attitude change when the arm moves along an infinitely small closed curve and then modifies the path by the Newton method. The amplitude of the arm motion decreases with the magnitude of the angular momentum, which shows that the proposed method utilizes the angular momentum effectively.     

Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots

This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.     

基于已知地形信息的海底机器人路径规划

路径规划是实现机器人智能化的重要组成部分,规划路径的优劣在很大程度上决定了机器人执行任务的效果. 传统的路径规划算法,例如基于图搜索的dijkstra算法和其改进后的A^*算法,以及基于采样的RRT （rapidlyexploring random tree） 算法和其改进后的 RRT^*算法,仅仅考虑了避障问题;基于插值曲线的算法可以产生较为光滑的轨迹;基于数值优化的算法可以将机器人速度、加速度等加入损失函数,通过优化求解,产生动力学特性较好的轨迹. 然而,面对当前越来越精确、丰富的先验地形信息,鲜有算法可以充分利用他们. 对此,基于海底数字高程地图（digital elevation map, DEM）,提出扩展A^*算法及FM （fast marching）算法改进算法,能够利用先验地形信息提高路径规划的效果. 通过仿真分析,对比3种算法:扩展A^*算法、TC FM （terrian cared fast marching）和TC FM^*算法,仿真结果表明,扩展A^*     

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance.

     

移动机器人动态人工势场路径规划方法研究

针对传统人工势场法在多障碍物环境中进行路径规划存在局部极小值,以及动态环境中移动机器人无法实时路径规划等问题,提出一种动态模糊控制的改进人工势场法,通过在势场力函数中引入速度矢量项,并采用模糊控制方法实时调节斥力场系数,从而实现在动态多障碍物的环境下机器人快速、实时、自适应的路径规划.仿真结果表明,该方法有效可行,在典型位置关系及特殊运动状态下均可获得良好的路径规划性能.

Abstract：

For traditional artificial potential field (APF) method,the local minima problem exists and can not satisfy the requirement of real-time for path planning of mobile robots in the multi-obstacle dynamic environment. A new APF method based on dynamic fuzzy control is proposed in this paper. The potential field force function is modified by introducing the velocity vector. The coefficients of potential field of repulsive force are adjusted by fuzzy control method. A fast real-time self-adaptive path planning method with dynamic multi-obstacle is realized. The simulation results show that the method is effective and feasible, and can get much better performance of path planning in typical location relationship and special motion relationship.     

Real-time map building and navigation for autonomous robots inunknown environments

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.     

基于运动微分约束的无人车辆纵横向协同规划算法的研究

为了满足在动态环境中快速行驶的要求,现有无人车辆普遍采用在传统规划系统的两层结构（路径规划-路径跟踪）之间增加局部规划的方法,通过在路径跟踪的同时进行避障来减少耗时的全局路径重规划. 本文针对这种三层结构规划系统存在的问题,提出基于运动微分约束的纵横向协同规划算法,在真实环境中实现速度不超过40km/h的无人驾驶. 根据车辆的实时运动状态,用高阶多项式模型在预瞄距离内对可行驶曲线进行建模,不仅使行驶过程中的转向平稳,而且在较高速时仍具有良好的路径跟踪能力. 由横向规划提供横向安全性的同时,在动力学约束的速度容许空间中进行纵向规划,实现平顺的加速与制动,并保证了纵向安全性和侧向稳定性. 该算法根据实时的局部环境自动决定纵横向期望运动参数,不需要人为设定行驶模式或调整参数. 采用该算法的无人驾驶平台在2011年和2012年智能车未来挑战赛的真实交通环境中,用统一的程序框架顺利完成全程的无人驾驶.     

Extending the motion planning framework—MoveIt with advanced manipulation functions for industrial applications

MoveIt is the primary software library for motion planning and mobile manipulation in ROS, and it incorporates the latest advances in motion planning, control and perception. However, it is still quite recent, and some important functions to build more advanced manipulation applications, required to robotize many manufacturing processes, have not been developed yet. MoveIt is an open source software, and it relies on the contributions from its community to keep improving and adding new features. Therefore, in this paper, its current state is analyzed to find out which are its main necessities and provide a solution to them. In particular, three gaps of MoveIt are addressed: the automatic tool changing at runtime, the generation of trajectories with full control over the end effector path and speed, and the generation of dual-arm trajectories using different synchronization policies. These functions have been tested with a Motoman SDA10F dual-arm robot, demonstrating their validity in different scenarios. All the developed solutions are generic and robot-agnostic, and they are openly available to be used to extend the capabilities of MoveIt.