基于改进PRM的采摘机器人机械臂避障路径规划

针对采摘机器人机械臂在不确定的环境中进行采摘作业的要求,提出了一种基于改进概率地图(PRM)算法的机械臂避障路径规划方法。将机械臂工作空间分割成离散单元集合,通过遍历的方法,获得机械臂工作空间中任意离散单元与机械臂有撞位姿之间的映射关系。将空间障碍物分割成离散单元,并通过索引映射关系获得与障碍物有撞的所有机械臂位姿信息,并以此建立关节构形空间。通过PRM算法在关节构形空间中快速搜索机械臂避障路径。仿真结果表明:相比传统PRM算法,改进算法速度提高22. 2%,能够有效地实现机械臂无碰撞路径规划。     

一种冗余机械臂的多运动障碍物避障算法

冗余机械臂的避障问题一直是工业机器人应用领域的研究热点之一;为了改进传统避障算法的不足,提出了一种多运动障碍物的避障算法;该算法利用各障碍物的运动状态得到与机械臂之间的最小预测距离,并将其利用雅可比转置矩阵转化为机械臂对应杆件上的躲避速度,再将躲避速度引入梯度投影法中求得机械臂的关节角速度,并通过积分得到避障运动中机械臂的关节角度值,在完成末端轨迹跟踪的同时实现冗余机械臂的实时避障;利用一款七自由度冗余机械臂对该算法进行了仿真验证,结果表明该算法能有效实现冗余机械臂对多运动障碍物的避障。     

基于人工势场一遗传算法的机械避障方法研究

人工势场法是机器人避障规划中常用的方法,具有简单、实时性强的特点,但其存在目标不可达和局部极小值问题;文中针对机械臂的避障问题,首先利用人工势场法确定机械臂末端的避障轨迹,使用改进的斥力场克服势场法的目标不可达问题,利用随机逃离和沿等势线逃离相结合的方法解决局部极小值问题;其次针对机械臂末端避障轨迹中的每个点,采用遗传算法计算其对应的机械臂逆运动学解,在确保杆件不发生碰撞的情况下,充分保持关节角变化的柔顺性;仿真结果表明算法能够有效地实现机械臂的避障规划.     

基于三维环境模型的启发式路径规划算法

针对多自由度机器人手臂在未知环境中实时避障的问题,提出了一种基于环境信息的连杆机器人实时路径规划方法。采用笛卡尔空间内的障碍物检测信息建立了障碍物的空间模型,并依据该模型设计一种基于启发式规则的机器人路径规划算法。该算法不断猜测和修正路径,通过模糊推理得到下一位姿点,通过曲线拟合得到到达该位姿点的路径。在Matlab下利用机器人工具箱建立了PUMA560型机器人的运动学模型,并在运动空间设置障碍物,对该算法进行仿真分析,分析结果说明所提出的路径规划算法可以在较短时间内完成避障运动,具有较好的实时性,同时运动关节的角度变化曲线比较平滑,运动中冲击力较小,这些特点使其便于在实际工程中使用。     

基于人工势场在机械臂避障路径规划中的研究综述

机械臂路径规划避障是目前机器人领域研究的热点,人工势场因算法简单、高效被越来越多地应用到机械臂路径规划避障中。本文首先介绍了人工势场的原理,分析了传统人工势场存在的目标不可达和局部极小值等问题,并对人工势场在机械臂避障规划中已有的算法进行分类综述,最后对人工势场在机械臂避障方面的发展趋势进行展望。     

基于改进比例导引法的机器人动态避障算法

随着机器人运动环境日益复杂,为了使机器人可以安全、有效地避开动态障碍到达目的地,提出一种基于改进比例导引法的机器人动态避障算法;首先借助比例导引法的思想,通过使机器人与动态障碍物的相对速度方向导引到避障向量方向完成避障,然后为满足避障完成时间和机器人机动性能约束要求,得到重叠比例导引系数取值范围,并采用比例导引法对机器人运动路径进行规划到达目的地,最后采用仿真实验测试其有效性;仿真结果表明,该算法可以使机器安全有效地避开动态障碍物,对机器人的实际运动轨迹控制具有一定的参考价值.     

面向复杂障碍场的电力巡检机器人局部动态融合路径规划

为解决电力巡检机器人在复杂障碍场中，常与障碍物碰撞、避障效率低等问题，提出面向复杂障碍场的电力巡检机器人局部动态融合路径规划方法。使用基于栅格法的复杂障碍场地图生成方法，构建面向复杂障碍场的电力巡检环境地图；结合所构建地图信息，由改进遗传算法寻优获取巡检所用全局最短路径后，经时间弹性带算法，结合不同时刻机器人位姿信息，由距离阈值判断机器人与动态障碍物碰撞可能性，以全局规划路径弹性拉伸的方式，完成局部动态融合的避障运行，且需分析局部动态规划路径中，机器人运行方向与全局规划路径一致性，动态调节规划机器人巡检路径。经测试，此方法使用后，机器人未出现碰撞问题，且避障速度提升约300%。     

非完整移动机械臂的避障运动规划

针对有空间障碍物避免的移动式操作机器人系统运动规划问题，提出了一种基于特殊的人工势函数，使用局部距离信息实现非完整移动机械臂系统实时避障运动规划方法，并且用Lyapunov定理证明了闲环系统的稳定性。用提出的方法对非完整移动机械臂系统进行仿真．仿真结果表明了它的正确有效性。     

基于遗传算法的机械臂实时避障路径规划研究

针对模块化机械臂在运行时可能与工作空间中的障碍物发生碰撞的问题, 提出一种基于遗传算法的避障路径规划算法。首先采用D-H(Denavit-Hartenberg)表示法对机械臂进行建模, 并进行运动学和动力学分析, 建立机械臂运动学和动力学方程。在此基础上, 利用遗传算法分别在单/多个障碍物工作环境中, 以运动的时间、移动的空间距离和轨迹长度作为优化指标, 实现机械臂避障路径规划的优化。通过仿真验证了基于遗传算法的机械臂避障路径规划算法的有效性与可行性, 该算法提高了运行中的机械臂有效避开工作空间中障碍物的效率。     

基于势场栅格法的移动机器人避障路径规划

针对传统人工势场法应用于移动机器人避障路径规划存在的缺陷,建立了改进的人工势场模型,通过在障碍物的斥力势场函数中增加最小安全距离,同时考虑机器人与目标点的相对距离,成功地解决了障碍物附近目标不可达(Good Nonreachable with Obstacles Nearby GNRON)的问题。此外,针对传统人工势场法的局部极小点和障碍物附近目标不可达同时存在的问题,提出了以改进人工势场法为主,栅格法为辅的方案来实施避障,使得机器人能够尽快地脱离局部极小并成功地绕过障碍物到达目标点。采用栅格法对改进人工势场法做辅助决策,弥补了改进人工势场法的不足,使机器人能够顺利到达势场的全局最小点,提高了避障路径规划的安全性和可达性。论文利用Matlab进行了算法仿真,结果证明了所提方法的正确性和有效性。     

Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

This paper proposes a decentralized behavior-based formation control algorithm for multiple robots considering obstacle avoidance. Using only the information of the relative position of a robot between neighboring robots and obstacles, the proposed algorithm achieves formation control based on a behavior-based algorithm. In addition, the robust formation is achieved by maintaining the distance and angle of each robot toward the leader robot without using information of the leader robot. To avoid the collisions with obstacles, the heading angles of all robots are determined by introducing the concept of an escape angle, which is related with three boundary layers between an obstacle and the robot. The layer on which the robot is located determines the start time of avoidance and escape angle; this, in turn, generates the escape path along which a robot can move toward the safe layer. In this way, the proposed method can significantly simplify the step of the information process. Finally, simulation results are provided to demonstrate the efficiency of the proposed algorithm.     

A neural network approach to complete coverage path planning.

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.     

基于改进人工势场法的机器人避障及路径规划研究

在不确定和复杂的移动环境中,利用传统的人工势场法进行机器人避障很难满足对环境动态适应性的需要。提出了一种相对速度的改进的人工势场法,针对于传统的路径规划中局部最小值问题,提出设置中间目标点的方法,给机器人一个外力以避免其在局部最小点处停止或者徘徊,确保机器人能够逃出最小值陷阱并顺利到达目标位置。最后在Matlab平台上进行了仿真实验,实验结果表明,改进后的人工势场法能较好地实现动态环境下移动机器人的路径规划。     

Real-time Velocity Alteration Strategy for Collision-free Trajectory Planning of Two Articulated Robot Manipulators

Two articulated robots working in a shared workspace can be programmed by planning the tip trajectory of each robot independently. To account for collision avoidance between links, a real-time velocity alteration strategy based on fast and accurate collision detection is proposed in this paper to determine the step of next motion of slave (low priority) robot for collision-free trajectory planning of two robots with priorities. The effectiveness of the method depends largely on a newly developed method of accurate estimate of distance between links. By using the enclosing and enclosed ellipsoids representations of polyhedral models of links of robots, the minimum distance estimate and collision detection between the links can be performed more efficiently and accurately. The proposed strategy is implemented in an environment where the geometric paths of robots are pre-planned and the preprogrammed velocities are piecewise constant but adjustable. Under the control of the proposed strategy, the master robot always moves at a constant speed. The slave robot moves at the selected velocity, selected by a tradeoff between collision trend index and velocity reduction in one collision checking time, to keep moving as far as possible and as fast as possible while avoid possible collisions along the path. The collision trend index is a fusion of distance and relative velocity between links of two robots to reflect the possibility of collision at present and in the future. Graphic simulations of two PUMA560 robot arms working in common workspace but with independent goals are conducted. Simulations demonstrate the collision avoidance capability of the proposed approach as compared to the approach based on bounding volumes. It shows that advantage of our approach is less number of speed alterations required to react to potential collisions.     

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.     

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。     

Adaptive path planning and obstacle avoidance for a robot with a large degree of redundancy

A new algorithm for path planning and obstacle avoidance for redundant planar robots is proposed. The task of path planning is formulated as a sequence of nonlinear programming problems. For each problem, the objective is to minimize the distance between the current location of the end-effector and a desired location. Two penalties are added to each objective function to ensure that the robot is not colliding with any obstacle and that its links are not crossed over. The effects of mechanical stops and limits for maximum joint movements are also incorporated as inequality constraints. The algorithm uses an adaptive scheme to activate the fewest number of the outboardmost joints, and none of the inboard ones if possible, to reach a desired location. The algorithm is especially useful when the number of joints is large. © 1996 John Wiley & Sons, Inc.     

栅格地图环境下机器人速度势实时路径规划

针对智能制造工程环境中移动机器人的自动避障问题,提出一种基于栅格地图的移动机器人速度势实时避障路径规划方法。利用栅格法二值化移动机器人的工作场景,从机器人中心出发向不同方向进行栅格搜索。基于障碍物对移动机器人有排斥作用以及目标点对机器人有吸引作用的思想,通过栅格数的累加计算机器人到障碍物之间的实时距离,并以此为参数,考虑障碍物的形状、最小安全距离等因素的影响来建立负的速度增量函数;以机器人当前位置与目标点的实时距离和角度为参数建立正向速度增量函数。进而在机器人运动学模型基础上,定义速度势函数来对移动机器人进行实时速度驱动。通过设置最小速度增量,避免在零势点处的局部极小点问题;通过设立距离阈值,避免在目标点附近速度增量趋于无穷的问题。通过仿真对所提出的算法进行验证。     

基于APF的AGV局部路径规划改进算法研究

针对原有人工势场法（artificial potential field,APF）在局部路径规划时的避障效果不良问题,提出一种APF-PSO的改进算法改善原算法优化路径规划的效果。将速度势场引入位置势场中使AGV(automated guided vehicle)动态避开不同速度的移动障碍物;当算法陷入局部最小值时,采取PSO(particle swarm optimization)算法,并对其惯性权重因子和学习因子做出调整,通过三次样条曲线插值来平滑路径,使得AGV找到最短路径。结果表明APFPSO改进算法可根据障碍物速度不同动态避障,解决了APF算法运算中避障效果不良问题。     

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。