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。     

空间机械臂在线实时避障路径规划研究

针对目前空间机械臂避障路径规划算法计算量大难以达到在线实时规划的缺点,对空间机械臂的在线实时避障路径规划问题进行了研究和探讨。采用规则体的包络对障碍物进行建模,并借助C空间法的思想,把障碍物和机械臂映射到两个相互垂直的平面内,将机械臂工作空间的三维问题转化为二维问题,并结合二岔树逆向寻优的方法进行路径搜索,从而大大减少了计算量,达到了在线实时规划的要求。最后在空间机器人仿真系统上对其进行了仿真研究,验证了该方法的可行性。     

基于改进势场法的机械臂动态避障规划

本文针对刚性机械臂提出了一种基于改进势场法的动态避障规划算法.势场产生只作用于机械臂末端的引力和作用于机械臂与障碍物最近点的排斥力,并依据动力学定律分别产生吸引速度和排斥速度.本文直接在笛卡尔空间中构造基于目标速度的吸引速度,使机械臂能够追踪动态目标;构造方向依据障碍物速度变化的排斥速度,使机械臂连杆也具备避开动态障碍的能力.最终两种速度在机械臂关节空间合成,为机械臂规划一条无碰路径并控制机械臂追踪动态目标.仿真结果证明了该方法的有效性.     

空间冗余机械臂路径规划方法研究

针对空间站遥操作7DOF冗余机械臂路径规划的安全性、可靠性问题,提出了基于臂型角逆运动学的优化A*路径规划算法.本文根据臂型角参数化完善了逆运动学方法,得到了32组完备逆解集,增加了路径规划时逆解选择的灵活性;通过臂型角搜索和最小奇异值优化A*路径规划算法,提高机械臂避障、避奇异能力,机械臂操作的灵活性和路径的安全可靠性;同时根据路径优化策略,有效平滑了路径,减少了机械臂的磨损.仿真结果说明了该方法的有效性.     

基于速度修正项的机械臂避障路径规划

针对机械臂运行过程中存在的碰撞问题, 提出一种基于速度修正项的机械臂避障路径规划方法. 利用B 样条曲线进行机械臂关节空间规划, 使机械臂能够在特定时刻运行到指定构型. 在运行过程中, 利用碰撞检测算法实时计算机械臂与障碍物的最小距离, 在碰撞即将发生时引入积分为零的避障速度修正项改变机械臂运行轨迹, 使得机械臂能够在实现障碍回避的同时, 保证其在特定时刻通过指定构型的要求. 仿真实验表明了所提出方法的正确性和有效性.     

改进人工势场的手术机器人位姿规划

运动避障与路径规划是手术机器人自动化手术中重要的技术环节,为机器人手术提供了良好的术中安全性及准确性.本研究针对上述两关键技术,提出改进人工势场的手术机器人位姿规划算法,首先,通过对引力函数进行改进,在不求取运动学逆解的情况下,能够准确驱动机械臂到达指定位姿;接着,利用快速凸包算法将障碍物凸体化,通过Gilbert Johnson Keerthi (GJK)算法计算障碍物与机械臂连杆等效圆柱面之间的最近距离,使避障距离更加准确;然后,通过自适应步长,使机械臂运动更加平稳快速;最后,引入动态引力常数,使机械臂具有逃离局部极小值的能力.实验结果表明,本研究能够让机器人在避障情况下平稳快速到达规划位姿,并在陷入局部极小值时逃逸,为未来医疗机器人在自动化手术方面提供了新思路.     

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。     

基于障碍物运动预测的移动机器人路径规划

针对移动机器人局部动态避障路径规划问题开展优化研究。基于动态障碍物当前历史位置轨迹,提出动态障碍物运动趋势预测算法。在移动机器人的动态避障路径规划过程中,考虑障碍物当前的位置,评估动态障碍物的移动轨迹;提出改进的D*Lite路径规划算法,大幅提升机器人动态避障算法的效率与安全性。搭建仿真验证环境,给出典型的单动态障碍物、多动态障碍物场景,对比验证了避障路径规划算法的有效性。     

基于运动学模型重构的单关节故障机械臂容错路径规划

针对单关节故障机械臂的路径规划问题,提出一种基于运动学模型重构的容错路径规划方法.首先基于旋量理论进行单关节故障机械臂的通用运动学模型重构;然后分析机械臂的退化工作空间,并以运动性能平稳为约束对其进行栅格化处理;最后通过改进传统的A$^\ast$算法,在退化工作空间中搜索出能够满足任务要求的轨迹.以七自由度机械臂为对象进行仿真实验,验证了所提出方法的正确性和有效性.     

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。     

基于解析法和遗传算法的机械手运动学逆解

研究优化机械手轨迹规划问题,机械手运动时要具有稳定性避障性能。针对平面3自由度冗余机械手优化控制问题,建立机械手的结构模型。提出用解析法和遗传算法相结合满足具有计算量小和适应性强的特点。在给定机械手末端执行器的运动轨迹,按着机械手冗余自由度,运动轨迹上每个点对应的关节角有无穷多个解。而通过算法可以找到一组最优的关节角,可得到优化机械手运动过程中柔顺性和避障点。仿真结果表明,该算法可以快速收敛到全局最优解,可用于计算冗余机械手运动学逆解,并可实现机器人的轨迹规划和避障优化控制。     

A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

This article presents a genetic algorithm approach to multi-criteria motion planning of mobile manipulator systems. For mobile robot path planning, traveling distance and path safety are considered. The workspace of a mobile robot is represented as a grid by cell decomposition, and a wave front expansion algorithm is used to build the numerical potential fields for both the goal and the obstacles. For multi-criteria position and configuration optimization of a mobile manipulator, least torque norm, manipulability, torque distribution and obstacle avoidance are considered. The emphasis of the study is placed on using genetic algorithms to search for global optimal solutions and solve the minimax problem for manipulator torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than the conventional methods.     

POMDP-APF：一种基于POMDP模型的APF无人机路径规划策略

针对无人机在路径规划过程中会遇到静态或者动态的障碍物,从而导致路径规划失败的问题,提出一种基于部分可观测马尔可夫决策过程（partially observable markov decision process,POMDP）模型的人工势场（artificial potential field,APF）无人机路径规划策略（POMDP-APF）。首先使用传感器获得的障碍物信息结合POMDP模型预测障碍物的未来位置,为无人机的路径规划做准备;其次,提出一种新的基于障碍物的正方体外接球的模型,保障无人机在路径规划过程中的安全性;最后,结合改进的APF算法实现无人机的路径规划。仿真结果表明,POMDP-APF策略在无人机实时路径规划中具有良好的可行性和有效性,使无人机能够有效避开障碍物,同时路径长度以及耗费时间更短。     

Trajectory Planning with Collision Avoidance for Redundant Robots Using Jacobian and Artificial Potential Field-based Real-time Inverse Kinematics

This study proposes an algorithm for combining the Jacobian-based numerical approach with a modified potential field to solve real-time inverse kinematics and path planning problems for redundant robots in unknown environments. With an increase in the degree of freedom (DOF) of the manipulator, however, the problems in realtime inverse kinematics become more difficult to solve. Although the analytical and geometrical inverse kinematics approach can obtain the exact solution, it is considerably difficult to solve as the DOF increases, and it necessitates recalculations whenever the robot arm DOF or Denavit-Hartenberg (D-H) parameters change. In contrast, the numerical method, particularly the Jacobian-based numerical method, can easily solve inverse kinematics irrespective of the aforementioned changes including those in the robot shape. The latter method, however, is not employed in path planning for collision avoidance, and it presents real-time calculation problems. This study accordingly proposes the Jacobian-based numerical approach with a modified potential field method that can realize real-time calculations of inverse kinematics and path planning with collision avoidance irrespective of whether the case is redundant or non-redundant. To achieve this goal, the use of a judgment matrix is proposed for obstacle condition identification based on the obstacle boundary definition; an approach for avoiding the local minimum is also proposed. After the obstacle avoidance path is generated, a trajectory plan that follows the path and avoids the obstacle is designed. Finally, the proposed method is evaluated by implementing a motion planning simulation of a 7-DOF manipulator, and an experiment is performed on a 7-DOF real robot.

     

基于遗传算法和B 样条曲线的平滑避障路径规划

传统的避障路径规划中常常存在不连续点,提出一种新的平滑避障路径规划的方法—遗传算法和B样条曲线法。首先,先通过碰撞侦测,能够侦测出前进路径中的障碍物,然后通过遗传算法再结合B样条曲线规划出平滑的避障路径。该算法可以避免运动过程因打滑而造成与目标位置的误差,解决机器人在静态环境中全局、局部路径规划和规划路径中存在不连续点的问题。仿真和实验结果证明了算法的有效性。     

装配机器人系统快速碰撞检测算法

提出一种面向操作手段装配系统的快速碰撞检测算法。该算法以机器人运动学和空间解析几何为基础,将判断机械手手臂与障碍物是否发生碰撞问题转化为直线段与有界平面是否存在公共点的简单解析几何问题,并以PUMA560操作手为例对算法加以说明,该算法不仅适用于静态的障碍物已知的环境,而且适用于障碍物运动规律已知的动态环境,减少了碰撞检测占用的时间,提高了路径规划的效率。     

Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning

This paper proposes a new approach for solving the problem of obstacle avoidance during manipulation tasks performed by redundant manipulators. The developed solution is based on a double neural network that uses Q-learning reinforcement technique. Q-learning has been applied in robotics for attaining obstacle free navigation or computing path planning problems. Most studies solve inverse kinematics and obstacle avoidance problems using variations of the classical Jacobian matrix approach, or by minimizing redundancy resolution of manipulators operating in known environments. Researchers who tried to use neural networks for solving inverse kinematics often dealt with only one obstacle present in the working field. This paper focuses on calculating inverse kinematics and obstacle avoidance for complex unknown environments, with multiple obstacles in the working field. Q-learning is used together with neural networks in order to plan and execute arm movements at each time instant. The algorithm developed for general redundant kinematic link chains has been tested on the particular case of PowerCube manipulator. Before implementing the solution on the real robot, the simulation was integrated in an immersive virtual environment for better movement analysis and safer testing. The study results show that the proposed approach has a good average speed and a satisfying target reaching success rate.     

A fast approach for the robust trajectory planning of redunant robot manipulators

In this article, a fast approach for robust trajectory planning, in the task space, of redundant robot manipulators is presented. The approach is based on combining an original method for obstacle avoidance by the manipulator configuration with the traditional potential field approach for the motion planning of the end-effector. This novel method is based on formulating an inverse kinematics problem under an inexact context. This procedure permits dealing with the avoidance of obstacles with an appropriate and easy to compute null space vector; whereas the avoidance of singularities is attained by the proper pseudoinverse perturbation. Furthermore, it is also shown that this formulation allows one to deal effectively with the local minimum problem frequently associated with the potential field approaches. The computation of the inverse kinematics problem is accomplished by numerically solving a linear system, which includes the vector for obstacle avoidance and a scheme for the proper pseudoinverse perturbation to deal with the singularities and/or the potential function local minima. These properties make the proposed approach suitable for redundant robots operating in real time in a sensor-based environment. The developed algorithm is tested on the simulation of a planar redundant manipulator. From the results obtained it is observed that the proposed approach compares favorably with the other approaches that have recently been proposed. © 1995 John Wiley & Sons, Inc.     

基于遗传算法的移动机器人路径规划

采用动态可变长编码的方法,以栅格表示环境。针对遗传算法大型障碍物难的问题,采用follow wall行为,较好地解决了基于遗传算法的快速路径规划和大型障碍物避障问题。该算法适应任何形状的障碍物,适用于静态和动态环境中。计算机仿真表明,该算法是一种正确和高效的路径规划方法。