未知环境下移动机器人安全路径规划的一种神经网络方法

针对未知环境下移动机器人的安全路径规划,采用了一种局部连接Hopfield神经网 络(Hopfield Neural Networks,HNN)规划器;分析了HNN稳定性,并给出了存在可行路 径的条件.如果存在可行路径,该方法不存在非期望的局部吸引点,并在连接权设计中兼顾 "过近"和"过远"来形成安全路径.为在单处理器上有效地在线路径规划,采用多顺序的 Gauss-Seidel迭代方法来加速HNN势场的传播.结果表明该方法具有较高的实时性和环境 适应性.     

基于变维度状态空间的增量启发式路径规划方法研究

在移动机器人路径规划中需要考虑运动几何约束,同时,由于它经常工作于动态、时变的环 境中,因此,还必须保证路径规划算法的效率.本文提出了一种基于变维度状态空间的增量启发式路径规划 方法,该方法既能满足移动机器人的运动几何约束,又能保证规划算法的效率.首先,设计了变维度状态空间, 在机器人周围的局部区域考虑运动几何约束组织高维状态空间,其他区域组织低维状态空间;然后,基于变维 度状态空间,提出了一种增量启发式路径规划方法,该方法在新的规划进程中可以使用以前的规划结果,仅对 机器人周围的局部区域进行重搜索,从而能保证算法的增量性及实时性;最后,通过仿真计算和机器人实验验 证了算法的有效性.     

动态不确定环境下移动机器人的在线实时路径规划

提出一种基于极坐标空间的、以机器人期望运动方向角为路径优化指标的动态不确定环境下移动机器人的在线实时路径规划方法。该法通过机器人的传感器系统,实时探测局部环境信息,在每一采样时刻,机器人首先对视野内的动态障碍物的位置进行采样,然后根据所采样的位置信息,利用自回归模型预测出下一采样时刻动态障碍物的位置,再将预测位置上的动态障碍物当作静态障碍物来处理,然后对其规划避碰路径,从而将动态路径规划转化为静态路径规划。仿真和实验结果验证了该方法有效可行,具有实时规划性和良好的避障能力。     

基于sDaCe-time的二叉树表示动态环境中的路径规划

研究具有空间和时间的space-time三维动态环境下的机器人路径规划,分析了四义树表示二维空间的搜索算法,在此基础上,提出采用二叉树表示二维空间的方法,时间信息中增加加速度,利用二叉树遍历方法和A*算法,设计一个在动态障碍物环境下进行路径规划的新算法,并在足球机器人系统中进行仿真,实现了较好的路径规划.     

FMM与改进GBNN模型相结合的多AUV实时围捕算法

多自主水下机器人(AUV)实时围捕是一个综合的研究课题,包括联盟生成和目标追捕等阶段.首先,基于快速行进算法(FMM)预估围捕时间,有效形成多AUV的动态围捕联盟;然后,在追捕阶段,AUV需要立即跟踪智能逃逸机器人以防止其逃跑.为了实现这一目标,在GBNN(Glasius biological inspired neural network)模型中使用反比例函数替换指数函数计算神经元连接权值,加入额外的衰减项,并提出两点加快神经元活性传播的改进措施,使其适用于实时追捕路径规划.仿真研究表明,围捕联盟形成机制和反比例权值GBNN模型实时路径规划策略都显示出其优越性.在水下环境的多AUV协作围捕中,所提出的围捕控制算法可以提高围捕效率,减少AUV所花费的追捕距离和逃逸机器人的逃逸距离.     

基于space-time的二叉树表示动态环境中的路径规划

研究具有空间和时间的三维动态环境下的机器人路径规划分析了四叉树表示二维空间的搜索算法,在此基础上,提出采space-time,用二叉树表示二维空间的方法时间信息中增加加速度利用二叉树遍历方法和算法设计一个在动态障碍物环境下进行路径规划的新算法并,,A*,,在足球机器人系统中进行仿真,实现了较好的路径规划。     

基于虚拟势场法的全局收敛视觉路径规划

作为一种局部路径规划策略, 虚拟势场法由于其简单易用, 效果良好而在机器人领域得到了广泛的应用. 但是这种方法的缺点是在路径规划的过程中, 机器人经常会陷入局部极小. 本文提出了一种基于虚拟势场法的视觉路径规划策略, 可以保证图像中的特征点在伺服过程中始终保持在摄像机的视野之内, 并且通过稳定性分析证明了该策略具有全局收敛性. 另外, 本文进一步探讨了视觉伺服中另一个常见问题: 如何在规划过程中得到较好的三维运动轨迹. 最后用仿真结果验证了本文所提出的路径规划策略具有良好的性能.     

关于智能机器人局部混沌评价全覆盖路径规划

针对智能机器人全覆盖路径规划问题,提出了一种局部混沌评价规划方法.考虑到机器人移动过程中的随机性与不可预知性,设计了具有反馈控制变量的四维混沌系统.将该系统与机器人运动模型融合,建立得到路径规划模型,同时引入耦合控制参数对系统误差进行调节,根据机器人的起始坐标和混沌起始状态参量,利用微分离散化处理便可计算出移动的路径点.考虑到路径规划的局部最优解,对机器人移动空间进行网格划分,根据激励计算动态网格活性值,利用网格活性对移动路径规划采取分流,进而得到分流后的局部路径与角位移变化量.与此同时,针对局部路径规划设计了相应的指标评价,用以校正规划结果.仿真结果表明,提出的局部混沌评价规划方法具有良好的路径全覆盖效果,同时获得了更低的路径重复率、移动距离,以及路径规划时间,有效提高了机器人的移动效率与控制平稳性.     

实现机器人动态路径规划的仿真系统

针对机器人动态路径规划问题,提出了在动态环境中移动机器人的一种路径规划方法,适用于环境中同时存在已知和未知,静止和运动障碍物的复杂情况。采用栅格法建立机器人空间模型,整个系统由全局路径规划和局部避碰规划两部分组成。在全局路径规划中,用快速搜索随机树算法规划出初步全局优化路径,局部避碰规划是在全局优化路径的同时,通过基于滚动窗口的环境探测和碰撞规则,对动态障碍物实施有效的局部避碰策略,从而使机器人安全顺利地到达目的地。仿真实验结果说明该方法具有可行性。     

一种改进的机器人滚动路径规划算法

针对静态和动态障碍物共存环境中机器人滚动路径规划的鲁棒性问题,提出了通过确定局部子目标位置判断机器人行进路线的路径规划算法.机器人以滚动窗口的形式实时检测局部环境信息,寻找并确定局部子目标的位置,从而做出下一步安全路径规划.机器人不断重复该过程,最终沿着一条优化路径安全到达目标点.仿真结果表明:该算法能使机器人沿着优化...     

A geometric approach to solving the stable workspace of quadruped bionic robot with hand–foot-integrated function

This paper discusses stable workspace of a hand–foot-integrated quadruped walking robot, which is an important issue for stable operation of the robot. This robot was provided with combined structure of parallel and serial mechanisms, whose stable workspace was the subspace of the workspace in which the system was considered stable. The reachable region was formed under structural conditions, while the stable space was formed by the overall conditions of stability which changed with the robot's pose and the mass of grabbed object. In this paper, based on the robot's main structure, the key issues in solving the robot's workspace are discussed in detail, including searching steady conditions of operation of the robot. To research the robot's workspace, working leg's motion curve needed to be solved by kinematics analysis. Due to the redundant drive, it was problematic to deal analytically with the kinematics of the quadruped walking robot. A geometric method of kinematic analysis was proposed as well. Based on the geometric method, the workspace of the robot under varying postures was analyzed by the method of grid partition and in combination with Matlab, VB and Solidworks software programs. An automated computational system of the stable workspace was developed and an example was given to illustrate the whole process in detail. The theory and analysis procedures were also verified by simulation of the robot and its actual grabbing of an object.     

Obstacle Modelling Oriented to Safe Motion Planning and Control for Planar Rigid Robot Manipulators

Trajectory planning and tracking are crucial tasks in any application using robot manipulators. These tasks become particularly challenging when obstacles are present in the manipulator workspace. In this paper a n-joint planar robot manipulator is considered and it is assumed that obstacles located in its workspace can be approximated in a conservative way with circles. The goal is to represent the obstacles in the robot configuration space. The representation allows to obtain an efficient and accurate trajectory planning and tracking. A simple but effective path planning strategy is proposed in the paper. Since path planning depends on tracking accuracy, in this paper an adequate tracking accuracy is guaranteed by means of a suitably designed Second Order Sliding Mode Controller (SOSMC). The proposed approach guarantees a collision-free motion of the manipulator in its workspace in spite of the presence of obstacles, as confirmed by experimental results.     

The Approximate Cell Decomposition with Local Node Refinement Global Path Planning Method: Path Nodes Refinement and Curve Parametric Interpolation

The paper presents a novel global path planning approach for mobile robot navigation in two dimensional workspace cluttered by polygonal obstacles. The core of the planning method introduced is based on the approximate cell decomposition method. The advantage of the new method is the employment of novel path refinement procedures of the paths produced by approximate cell decomposition that are based on local characteriscics of the workspace. Furthermore, the refined path is parametrically interpolated by cubic splines via a physical centripetal model, introducing the dynamic constraints of mobile robots' motion to the path construction. The method has been implemented both in a computer graphics simulation and on a real mobile robot cruising at indoor environments. Planned paths on several configurations are presented.     

A Shortest Path Based Path Planning Algorithm for Nonholonomic Mobile Robots

A path planning algorithm for a mobile robot subject to nonholonomic constraints is presented. The algorithmemploys a global- local strategy, and solves the problem in the 2D workspace of the robot, without generating the complexconfiguration space. Firstly, a visibility graph is constructed for finding a collision-free shortest path for a point. Secondly,the path for a point is evaluated to find whether it can be used as a reference to build up a feasible path for the mobile robot.If not, this path is discarded and the next shortest path is selected and evaluated until a right reference path is found. Thirdly,robot configurations are placed along the selected path in the way that the robot can move from one configuration to the nextavoiding obstacles. Lemmas are introduced to ensure that the robot travels using direct, indirect or reversal manoeuvres. Thealgorithm is computationally efficient and runs in time O(nk + n log n) for k obstacles andn vertices. The path found is near optimal in terms of distance travelled. The algorithm is tested in computersimulations and test results are presented to demonstrate its versatility in complex environments.     

A path conditioning method with trap avoidance

This work presents a sliding-mode method for robotic path conditioning. The proposal includes a trap avoidance algorithm in order to escape from trap situations, which are analogous to local minima in potential field-based approaches. The sliding-mode algorithm activates when the desired path is about to violate the robot workspace constraints, modifying it as much as necessary in order to fulfill all the constraints and reaching their limit surface at low speed. The proposed path conditioning algorithm can be used on-line, since it does not require a priori knowledge of the desired path, and improves the conventional conservative potential field-based approach in the sense that it fully exploits the robot workspace. The proposed approach can be easily added as an auxiliary supervisory loop to conventional robotic planning algorithms and its implementation is very easy in a few program lines of a microprocessor. The proposed path conditioning is compared through simulation with the conventional potential field-based approach in order to show the benefits of the method. Moreover, the effectiveness of the proposed trap avoidance algorithm is evaluated by simulation for various trap situations.     

3D Collision-Free Motion Based on Collision Index

A collision-free motion planning method for mobile robots moving in 3-dimensional workspace is proposed in this article. To simplify the mathematical representation and reduce the computation complexity for collision detection, objects in the workspace are modeled as ellipsoids. By means of applying a series of coordinate and scaling transformations between the robot and the obstacles in the workspace, intersection check is reduced to test whether the point representing the robot falls outside or inside the transformed ellipsoids representing the obstacles. Therefore, the requirement of the computation time for collision detection is reduced drastically in comparison with the computational geometry method, which computes a distance function of the robot segments and the obstacles. As a measurement of the possible occurrence of collision, the collision index, which is defined by projecting conceptually an ellipsoid onto a 3-dimensional Gaussian distribution contour, plays a significant role in planning the collision-free path. The method based on reinforcement learning search using the defined collision index for collision-free motion is proposed. A simulation example is given in this article to demonstrate the efficiency of the proposed method. The result shows that the mobile robot can pass through the blocking obstacles and reach the desired final position successfully after several trials.     

一种改进多机器人分布式滚动路径规划算法

针对多移动机器人全局静态环境未知的路径规划问题，采用了一个全局性能指标，在保证路径较优的情况下，最小化机器人的停顿时间，提出机器人之间以修正局部路径为主的协调策略。根据多机器人滚动路径算法的原理，设计了改进的多机器人分布式滚动路径规划算法。在已有仿真系统上进行测试，比较了所提出的协调策略与改变机器人移动速度协调策略对性能指标的影响。仿真结果表明，静态环境未知情况下，机器人可以并行规划各自的协调路径。     

Path feasibility and modification

Kinematic feasibility of a planned robot path is restrained by the kinematic constraints of the robot executing the task, such as workspace, configuration, and singularity. Since these kinematic constraints can be described utilizing the geometry of the given robot, corresponding regions within the robot workspace can be expressed in geometrical representation. Consequently, geometric information can be derived from the planned path and the geometric boundaries of these regions. Then, by utilizing the geometric information and proper modification strategies, a Cartesian robot path that is kinematically infeasible can be modified according to different task requirements. To demonstrate the proposed feasibility and modification schemes, simulations for a 6R robot manipulator are executed.     

Reactive Path Planning in a Dynamic Environment

This paper deals with the problem of path planning in a dynamic environment, where the workspace is cluttered with unpredictably moving objects. The concept of the virtual plane is introduced and used to create reactive kinematic-based navigation laws. A virtual plane is an invertible transformation equivalent to the workspace, which is constructed by using a local observer. This results in important simplifications of the collision detection process. Based on the virtual plane, it is possible to determine the intervals of the linear velocity and the paths that lead to collisions with moving obstacles and then derive a dynamic window for the velocity and the orientation to navigate the robot safely. The speed of the robot and the orientation angle are controlled independently using simple collision cones and collision windows constructed from the virtual plane. The robot's path is controlled using kinematic-based navigation laws that depend on navigation parameters. These parameters are tuned in real time to adjust the path of the robot. Simulation is used to illustrate collision detection and path planning.