基于自适应动态窗口改进细菌算法与移动机器人路径规划

针对移动机器人在复杂环境下的路径规划问题,提出一种新的自适应动态窗口改进细菌算法,并将新算法应用于移动机器人路径规划。改进细菌算法继承了细菌算法与动态窗口算法（dynamic window algorithm, DWA）在避障时的优点,能较好实现复杂环境中移动机器人静态和动态避障。该改进算法主要分三步完成移动机器人路径规划。首先,利用改进细菌趋化算法在静态环境中得到初始参考规划路径。接着,基于参考路径,机器人通过自身携带的传感器感知动态障碍物进行动态避障并利用自适应DWA完成局部动态避障路径规划。最后,根据移动机器人局部动态避障完成情况选择算法执行步骤,如果移动机器人能达到最终目标点,结束该算法,否则移动机器人再重回初始路径,直至到达最终目标点。仿真比较实验证明,改进算法无论在收敛速度还是路径规划精确度方面都有明显提升。     

Velocity potential approach to path planning for avoiding moving obstacles

This paper describes the theory and an experiment of a velocity potential approach to path planning and avoiding moving obstacles for an autonomous mobile robot by use of the Laplace potential. This new navigation function for path planning is feasible for guiding a mobile robot avoiding arbitrarily moving obstacles and reaching the goal in real time. The essential feature of the navigation function comes from the introduction of fluid flow dynamics into the path planning. The experiment is conducted to verify the effectiveness of the navigation function for obstacle avoidance in a real world. Two examples of the experiment are presented; first, the avoidance of a moving obstacle in parallel line-bounded space, and second, the avoidance of one moving obstacle and another standing obstacle. The robot can reach the goal after successfully avoiding the obstacles in these cases.     

融合动态障碍物运动信息的路径规划算法

传统的路径规划算法只能在障碍物不发生位置变化的环境中计算最优路径。但是随着机器人在商场、医院、银行等动态环境下的普及,传统的路径规划算法容易与动态障碍物发生碰撞等危险。因此,关于随机动态障碍物条件下的机器人路径规划算法需要得到进一步改善。为了解决在动态环境下的机器人路径规划问题,提出了一种融合机器人与障碍物运动信息的改进动态窗口法来解决机器人在动态环境下的局部路径规划问题,并且与优化A*算法相结合来实现全局最优路径规划。主要内容体现为：在全局路径规划上,采用优化A*算法求解最优路径。在局部路径规划上,以动态障碍物的速度作为先验信息,通过对传统动态窗口法的评价函数进行扩展,实现机器人在动态环境下的自主智能避障。实验证明,该算法可以实现基于全局最优路径的实时动态避障,具体表现为可以在不干涉动态障碍物的条件下减少碰撞风险、做出智能避障且路径更加平滑、长度更短、行驶速度更快。     

融合安全A*算法与动态窗口法的机器人路径规划

A^*算法通过启发信息指引搜索方向,被广泛应用于移动机器人的路径规划,但其规划出的搜索路径存在冗余节点且与障碍物相近,无法满足动态避障需求。对标准A^*算法进行改进,设计安全A^*算法并融合动态窗口法进行路径规划。定义安全距离因子引入A^*算法的启发函数中,提高算法规划路径的安全性,同时采用平面结构法对算法规划得到的路径进行优化,根据相邻节点与障碍物之间的位置关系判断该相邻节点间是否存在障碍物,由此减少路径拐点数,提高路径平滑度。由于当移动机器人处于未知环境时,仅靠A^*算法不能避开障碍物到达目标点,因此借助动态窗口法的局部避障功能。通过安全A^*算法规划全局最优路径节点坐标,设计融合子函数改进动态窗口法的评价函数,解决动态窗口法易陷入局部最优的问题。实验结果表明,在复杂环境中,该方法通过融合安全A^*算法和动态窗口法,能够确保在安全路径基础上实时随机避障,使机器人安全到达终点。     

基于几何法的移动机器人路径规划

旨在解决动态环境中移动机器人与障碍物发生碰撞可能性的判断和避开障碍的路径规划。提出了采用几何计算的方法判断机器人和障碍物之间发生碰撞的条件,规划出机器人沿着收敛曲线运动到安全圆周,在安全圆周上作动态圆周运动,最后沿着圆弧退出圆周到达预定的避障路径。将基本的避开障碍的理论和几何算法有机地结合起来,获得了光滑的路径,提高了机器人避开障碍的效率。     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

基于混合势场法的移动机器人路径规划

针对目前移动机器人在路径规划中出现的问题,提出一种自主移动机器人路径规划的新方法——混合势场法。分析了人工势场法的不足,找出局部极小值点的形成原因;针对人工势场法中障碍物附近目标不可达问题,采用了在斥力场函数中加入斥力因子,使得机器人顺利到达目标点;针对陷入局部极小值和振荡的问题,提出了混合势场法,通过将势场法和可视图法结合起来,使得机器人走出局部极小值和振荡区域。最后,将混合势场法应用于室内移动机器人的路径规划中,仿真实验证明了该方法的有效性。     

粒子群优化的移动机器人路径规划算法

针对移动机器人在复杂环境下采用传统方法路径规划收敛速度慢和局部最优问题,提出了斥力场下粒子群优化（PSO）的移动机器人路径规划算法。首先采用栅格法对机器人的移动路径进行初步规划,并将栅格法得到的初步路径作为粒子的初始种群,根据障碍物的不同形状和尺寸以及障碍物所占的地图总面积确定栅格粒度的大小,进而对规划路径进行数学建模;然后根据粒子之间的相互协作实现对粒子位置和速度的不断更新;最后采用障碍物斥力势场构造高安全性适应度函数,从而得到一条机器人从初始位置到目标的最优路径。利用Matlab平台对所提算法进行仿真,结果表明,该算法可以实现复杂环境下路径寻优和安全避障;同时还通过对比实验验证了算法收敛速度快,能解决局部最优问题。     

Velocity planning for a mobile robot to track a moving target — a potential field approach

Potential field method has been widely used for mobile robot path planning, but mostly in a static environment where the target and the obstacles are stationary. The path planning result is normally the direction of the robot motion. In this paper, the potential field method is applied for both path and speed planning, or the velocity planning, for a mobile robot in a dynamic environment where the target and the obstacles are moving. The robot’s planned velocity is determined by relative velocities as well as relative positions among robot, obstacles and targets. The implementation factors such as maximum linear and angular speed of the robot are also considered. The proposed approach guarantees that the robot tracks the moving target while avoiding moving obstacles. Simulation studies are provided to verify the effectiveness of the proposed approach.     

改进人工势场法的移动机器人路径规划

传统人工势场法在路径规划过程中易陷入势场局部最小点和陷阱区域,面对较为复杂的障碍物环绕环境也难以规划出完整路径。针对这个问题,提出了一种改进人工势场法。引入机器人前进的方向向量,对斥力的生成和计算机制进行了调整以解决其处于局部最小点情况下无法继续规划路径的问题;添加了判断机制以识别周边环境状况,当机器人处于陷阱区域等复杂环境下时设立虚拟目标点以引导其向外运动从而摆脱陷阱区域。结果表明,改进算法可以有效解决传统算法容易出现的路径规划中断情况;同时与传统算法相比,其在随机障碍物环境中的规划路径长度减少,有效提高了路径规划效率。     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.     

基于多行为的移动机器人路径规划

机器人由当前点向目标点运动的过程中,所处环境经常为动态变化且未知的,这使得传统的路径规划算法对于移动机器人避障过程很难建立精确的数学模型.为此,针对环境信息完全未知的情况,为移动机器人设计一种基于模糊控制思想的多行为局部路径规划方法.该方法通过对各种行为之间进行适时合理的切换,以保证机器人安全迅速地躲避静态和动态障碍物,并利用改进的人工势场法实现对变速目标点的追踪.对于模糊避障中常见的U型陷阱问题,提出一种边界追踪的陷阱逃脱策略,使得机器人成功解除死锁状态.另外,设计一个速度模糊控制器,实现了机器人的智能行驶.最后,基于Matlab平台的仿真结果验证了所提出算法的有效性和实时性,与A*势场法的对比结果更突出了该算法的可行性.     

基于模糊势场法的移动机器人局部路径规划

针对采用传统人工势场法进行移动机器人局部路径规划时存在的局部极小点和规划路径过长等问题,提出了一种基于虚拟目标点和有限状态机的模糊势场法。构造基于人工势场的虚拟目标点法来解决局部极小点问题,在合适的位置设置虚拟目标点使机器人逃离局部极小点区域。将虚拟目标点法与模糊控制相结合,对障碍物环境进行预测,及时避障,解决机器人在复杂环境中采用虚拟目标点法规划路径时存在的路径过长问题。设计一个有限状态机来判断障碍物环境,执行算法转换策略,使改进算法适用于多种复杂环境。所设计算法在MATLAB平台上进行了仿真验证。结果表明,该算法能够使机器人逃出局部极小点、缩短规划路径。算法不仅适用于简单、离散环境,在传统算法运行困难的、复杂的环境中,例如墙型、U型和多U型障碍物环境,也能规划出可行的优化路径。     

Neural computation for collision-free path planning

Automatic path planning plays an essential role in planning of assembly or disassembly of products, motions of robot manipulators handling part, and material transfer by mobile robots in an intelligent and flexible manufacturing environment. The conventional methodologies based on geometric reasoning suffer not only from the algorithmic difficulty but also from the excessive time complexity in dealing with 3-D path planning. This paper presents a massively parallel, connectionist algorithm for collision-free path planning. The path planning algorithm is based on representing a path as a series ofvia points or beads connected by elastic strings which are subject to displacement due to a potential field or a collision penalty function generated by polyhedral obstacles. Mathematically, this is equivalent to optimizing a cost function, defined in terms of the total path length and the collision penalty function, by moving thevia points simultaneously but individually in the direction that minimizes the cost function. Massive parallelism comes mainly from: (1) the connectionist model representation of obstacles and (2) the parallel computation of individualvia-point motions with only local information. The algorithm has power to deal effectively with path planning of three-dimensional objects with translational and rotational motions. Finally, the algorithm incorporates simulated annealing to solve a local minimum problem. Simulation results are shown.     

Trajectory time scaling of a mobile robot to avoid dynamic obstacles on the basis of the INLVO

To ensure the collision safety of mobile robots, the velocity of dynamic obstacles should be considered while planning the robot’s trajectory for high-speed navigation tasks. A planning scheme that computes the collision avoidance trajectory by assuming static obstacles may result in obstacle collisions owing to the relative velocities of dynamic obstacles. This article proposes a trajectory time-scaling scheme that considers the velocities of dynamic obstacles. The proposed inverse nonlinear velocity obstacle (INLVO) is used to compute the nonlinear velocity obstacle based on the known trajectory of the mobile robot. The INLVO can be used to obtain the boundary conditions required to avoid a dynamic obstacle. The simulation results showed that the proposed scheme can deal with typical collision states within a short period of time. The proposed scheme is advantageous because it can be applied to conventional trajectory planning schemes without high computational costs. In addition, the proposed scheme for avoiding dynamic obstacles can be used without an accurate prediction of the obstacle trajectories owing to the fast generation of the time-scaling trajectory.     

基于改进蚁群算法的移动机器人平滑路径规划

针对移动机器人提出了基于改进蚁群算法的平滑路径规划方法。为了克服蚁群算 法解决路径规划问题时存在的收敛速度慢的缺点,对启发因子的矩阵初始值及更新方式进行了 改进,启发因子改进后的结果与之前相比,平均路径长度减少了 17.6%,平均收敛代数减少了 93.1%;对于栅格环境下存在障碍物时机器人累计转弯角度大的问题,提出了控制点转移策略, 在上一步改进的基础上,通过对控制路径走向的栅格中心点向栅格角顶点的转移,实现了路径 规划的平滑改进。路径规划仿真结果表明,与平滑改进前相比,平滑改进后机器人的平均路径 长度减少了 4.28%,累计转弯角度减少了 52.58%。     

A Generalized Real-Time Obstacle Avoidance Method Without the Cspace Calculation

An important concept proposed in the early stage of robot path planning field is the shrinking of a robot to a point and meanwhile the expanding of obstacles in the workspace as a set of new obstacles. The resulting grown obstacles are called the Configuration Space (Cspace) obstacles. The find-path problem is then transformed into that of finding a collision-free path for a point robot among the Cspace obstacles. However, the research experiences have shown that the Cspace transform is very hard when the following situations occur: 1) both the robot and obstacles are not polygons, and 2) the robot is allowed to rotate. This situation gets even worse when the robot and obstacles are three dimensional (3D) objects with various shapes. For this reason, direct path planning approaches without the Cspace transformation is quite useful and expected.Motivated by the practical requirements of robot path planning, a generalized constrained optimization problem (GCOP) with not only logic AND but also logic OR relationships was proposed and a mathematical solution developed previously. This paper inherits the fundamental ideas of inequality and optimization techniques from the previous work, converts the obstacle avoidance problem into a semi-infinite constrained optimization problem with the help of the mathematical transformation, and proposes a direct path planning approach without Cspace calculation, which is quite different from traditional methods. To show its merits, simulation results in 3D space have been presented.     

面向未知地图的六足机器人路径规划算法

针对移动机器人路径规划中无法准确得知全局地图的问题,提出了一种基于模糊规则和人工势场法的局部路径规划算法。首先,利用测距组与模糊规则,进行障碍物的形状分类,构建局部地图;其次,在人工势场法中引入了一种修正的斥力函数,基于局部地图,利用人工势场法进行局部路径规划;最后,随着机器人的运动,设置时间断点,以减少路径震荡。针对随机障碍物和凹凸障碍物的地图,分别采用传统人工势场法和改进的人工势场法进行仿真,其结果表明：在遇到随机障碍物时,相比传统人工势场法,改进的人工势场法能够显著减少与障碍物的碰撞;在遇到凹凸障碍物时,改进的人工势场法能够很好地完成路径规划的目标。所提算法对地形变化适应能力强,能够实现在未知地图下的六足机器人路径规划。     

Path deformation method for robot motion planning problems in the presence of obstacles

This paper studies the following path planning problem for a robot. There is a given path avoiding obstacles. If existing obstacles change their location or new obstacles appear, the preplanned path must be appropriately deformed for all obstacles avoidance. We develop a solution approach based on the component-wise method of smoothing the path curvature and the method of potentials. The proposed approach enables to avoid obstacles and smooth the path and its curvature (in the 2D and 3D cases).     

动态未知环境下一种新的机器人路径规划方法

针对机器人动态路径规划问题,提出了一种机器人在复杂动态环境中实时路径规划方法.该方法基于滚动窗口的路径规划和避障策略,通过设定可视点子目标、绕行障碍物和对动态障碍物的分析预测,实现机器人在复杂动态环境下的路径规划.针对障碍物分布情况,合理设计可视点法和绕行算法之间转换,有效地解决了局部路径规划的死循环与极小值问题.该方...