基于遗传算法的欠驱动机器人运动规划

研究了平面欠驱动机器人的非完整运动规划问题,建立欠驱动机器人系统动力学模型,提出了一种利用遗传算法(GA)解决欠驱动机器人运动规划的方法。引入部分稳定控制器的思想,提出基于能量最优的评价函数,利用遗传算法对评价函数进行离线优化,得到有关部分稳定控制器的切换规则。此方法可以推广到任意平面欠驱动机器人的规划问题上,以平面3R欠驱动机器人为研究对象进行了数值仿真,仿真结果验证了该方法的有效性。     

一种基于遗传算法的欠驱动机器人避障运动规划新方法*

对平面欠驱动机器人的避障运动规划问题进行了研究,提出了一种利用遗传算法解决此类系统避障问题的新方法。通过引入虚拟弹簧—阻尼系统,在障碍物存在的情况下对系统非完整约束方程的广义力进行修正,采用部分稳定规划器进行运动规划,建立了基于能量的适应度函数,并利用遗传算法对提出的适应度函数进行全局优化,得到了部分稳定规划器的最优切换顺序,进而实现了欠驱动机器人的无碰撞路径规划。最后以平面3R欠驱动机器人为例进行了仿真实验,验证了该方法的有效性。     

基于改进蚁群算法的四足机器人步态规划

四足机器人关节众多、运动方式复杂,步态规划是四足机器人运动控制的基础。传统的算法多基于仿生原理,缺乏广泛适应性。 在建立运动学方程的基础上,提出了一种基于改进蚁群算法的步态规划算法。该算法利用了四足机器人4条腿运动的线性无关性,将步态规划问题转换为在四维空间里求取最长路径问题。仿真结果表明,该算法得出了满足约束条件的所有步态,最后通过机器人样机检验,验证了该算法求取结果的有效性和合理性。     

一种基于遗传算法的欠驱动机器人避障运动规划新方法*

对平面欠驱动机器人的避障运动规划问题进行了研究,提出了一种利用遗传算法解决此类系统避障问题的新方法。通过引入虚拟弹簧—阻尼系统,在障碍物存在的情况下对系统非完整约束方程的广义力进行修正,采用部分稳定规划器进行运动规划,建立了基于能量的适应度函数,并利用遗传算法对提出的适应度函数进行全局优化,得到了部分稳定规划器的最优切换顺序,进而实现了欠驱动机器人的无碰撞路径规划。最后以平面3R欠驱动机器人为例进行了仿真实验,验证了该方法的有效性。     

广义Voronoi图求解多机器人运动规划

在拥挤环境中,由于障碍物的边界形状比较复杂,需要使用广义Voronoi图表示空间环境。且在多移动机器人的运动规划过程中,需要协调多个机器人的运动,必须得到Voronoi图通道的宽度。为此提出了一种计算拥挤障碍物环境中生成的广义Voronoi图及其通道宽度的算法。并在生成的Voronoi图上利用A*算法对多个机器人进行路径规划,并利用分布式方法协调多个机器人运动。对协调两个机器人运动的过程进行了仿真,仿真结果表明利用提出的算法生成的具有通道宽度信息的Voronoi图能够满足多移动机器人运动规划的需要。     

双机器人协作管管焊接的运动规划

针对中小管径管管相贯的马鞍形曲线焊缝,以双机器人协作为基础,提出一种应用于双机器人协作的焊接过程中机器人运动规划的方法。该方法基于焊缝、焊枪的数学模型,充分利用双机器人系统的冗余特性,提出了融合机器人操作度、关节位置等的多指标目标函数,以机器人关节极限为规划约束条件,采用遗传算法规划出最优机器人运动路径。在三维仿真环境中进行的仿真结果初步验证了该方法的可行性和有效性。     

Motion planning for cooperative unicycle-type mobile robots with limited sensing ranges: A distributed receding horizon approach

This paper presents a decentralized motion planner for a team of nonholonomic mobile robots subject to constraints imposed by sensors and the communication network. The motion planning scheme consists of decentralized receding horizon planners that reside on each vehicle to achieve coordination among flocking agents. The advantage of the proposed algorithm is that each vehicle only requires local knowledge of its neighboring vehicles. The main requirement for designing an optimal conflict-free trajectory in a decentralized way is that each robot does not deviate too far from its presumed trajectory designed without taking the coupling constraints into account. A comparative study between the proposed algorithm and other existing algorithms is provided in order to show the advantages, especially in terms of computing time. Finally, experiments are performed on a team of three mobile robots to demonstrate the validity of the proposed approach.     

基于动力学约束的机器人无碰运动规划

本文旨在通过分析机器人系统的动力学特性来研究机器人在其工作环境中如何避开障碍物且按照预定路径运动的轨迹规划问题.文中提出了一种综合考虑多种约束条件的线性规划算法,该算法能够连续地调整系统的内能,且给出预期的运动轨迹.该算法已在IBM-PC2/80机上实现,成功地应用于我国某核电站的一反应蒸发器中检测机器人的无碰三维运动规划.文中给出了这一规划的结果及其图形仿真.     

多机器人协作搬运路径规划研究

针对多机器人协作系统,提出了一种新的混合定点转动和遗传算法的方法,解决其协作路径规划问题。该方法利用遗传算法并行计算、不易陷入局部最优的优点,具备概率上寻找全局最优解的能力,同时结合了定点转动法易实现、有效减少单机器人路径浪费的优点。仿真实验结果表明,该规划方法运算速度较快,在得到有效规划路径的同时,也易于实现对单机器人的控制。     

基于混沌遗传的巡航导弹实时规划技术

研究针对突发威胁的实时规划技术能够提高巡航导弹在未知威胁环境中的生存能力,具有重大的研究价值。实时规划的规划思想和规划算法的设计问题是巡航导弹实时规划技术必须要解决的两大难题。基于解决问题的考虑,设计了实时航迹规划的规划思路和一种混沌遗传规划算法。基于传统遗传算法的思想,利用威胁和约束条件构建适应度函数对航迹进行评价,设计了一种特殊的实数结构体编码,提出了合适的选择、交叉、变异三种算子使种群进化,并引入了混沌搜索算子,加速种群的收敛。最后进行了实时航迹规划的仿真分析。仿真结果表明:实时规划的航迹能够避开地形和新出现的威胁,算法收敛速度较快。这说明,设计的算法能够满足当前未知威胁环境下巡航导弹实时规划的需要。     

Stepping over obstacles with humanoid robots

The wide potential applications of humanoid robots require that the robots can walk in complex environments and overcome various obstacles. To this end, we address the problem of humanoid robots stepping over obstacles in this paper. We focus on two aspects, which are feasibility analysis and motion planning. The former determines whether a robot can step over a given obstacle, and the latter discusses how to step over, if feasible, by planning appropriate motions for the robot. We systematically examine both of these aspects. In the feasibility analysis, using an optimization technique, we cast the problem into global optimization models with nonlinear constraints, including collision-free and balance constraints. The solutions to the optimization models yield answers to the possibility of stepping over obstacles under some assumptions. The presented approach for feasibility provides not only a priori knowledge and a database to implement stepping over obstacles, but also a tool to evaluate and compare the mobility of humanoid robots. In motion planning, we present an algorithm to generate suitable trajectories of the feet and the waist of the robot using heuristic methodology, based on the results of the feasibility analysis. We decompose the body motion of the robot into two parts, corresponding to the lower body and upper body of the robot, to meet the collision-free and balance constraints. This novel planning method is adaptive to obstacle sizes, and is, hence, oriented to autonomous stepping over by humanoid robots guided by vision or other range finders. Its effectiveness is verified by simulations and experiments on our humanoid platform HRP-2.     

基于QPSO算法的机器人时间最优轨迹规划

在考虑关节约束的前提下,为得到工业机器人时间最优的关节运动轨迹,提出一种工业机器人时间最优轨迹规划新算法。采用五次非均匀B样条插值法构造各关节运动轨迹,得到的机器人各关节位置准确,各关节速度、加速度和加加速度曲线连续。利用量子行为粒子群优化算法(Quantum-behaved Particle Swarm Optimization,简称QPSO)进行时间最优的轨迹规划,该算法可以在整个可行域上搜索,具有较强的全局搜索能力。与标准粒子群算法(Particle Swarm Optimization,简称PSO)和差分进化算法(Differential Evolution Algorithm,简称DE)相比较,结果显示使用该算法进行时间最优的轨迹规划得到的数值结果更小。     

平面复杂边缘的双机器人协调跟踪运动规划研究

研究双机器人协调跟踪平面复杂边缘所涉及的问题.在笛卡儿空间内对跟踪任务进行协调分解,并分析双机器人的位置约束关系,采用双曲线函数规划双机器人基于关节空间的运动轨迹,以满足位置、速度以及加速度等平滑运动的约束条件,采用基于曲线极大值的分段跟踪算法扩展平面复杂曲线的一般性.仿真实验和应用均表明了所研究内容的可行性和正确性.     

仿人机器人足迹规划建模及算法实现

足迹规划是仿人机器人运动规划领域的一个新思想.本文建立了仿人机器人足迹规划的模型,并通过构建启发式成本函数,利用A*算法予以实现.针对复杂多障碍物环境,特别提出了基于可变落地足迹数量的复合足迹转换模型的方法.仿真实验证明了规划模型和算法的有效性和完备性,规划效果达到仿人机器人在线运动规划的要求.同时,数值实验也证明了在复杂多障碍物环境下复合足迹转换模型的必要性和优越性.     

机器人运动规划方法的研究

针对路径规划以及碰撞检测这一研究的重点问题,提出了G-空间法、人工势力场法、遗传算法等。序列规划问题一般转化为旅行商问题来求解。在综合现有序列规划和路径规划方法的基础上,提出两种机器人运动规划算法：基于任意路径的运动规划算法和基于直线路径的运动规划算法,思路简单,能对各种机器人工程任务进行运动规划。     

Decentralized Motion Planning for Multiple Mobile Robots: The Cocktail Party Model

This paper presents an approach for decentralized real-time motion planning for multiple mobile robots operating in a common 2-dimensional environment with unknown stationary obstacles. In our model, a robot can see (sense) the surrounding objects. It knows its current and its target's position, is able to distinguish a robot from an obstacle, and can assess the instantaneous motion of another robot. Other than this, a robot has no knowledge about the scene or of the paths and objectives of other robots. There is no mutual communication among the robots; no constraints are imposed on the paths or shapes of robots and obstacles. Each robot plans its path toward its target dynamically, based on its current position and the sensory feedback; only the translation component is considered for the planning purposes. With this model, it is clear that no provable motion planning strategy can be designed (a simple example with a dead-lock is discussed); this naturally points to heuristic algorithms. The suggested strategy is based on maze-searching techniques. Computer simulation results are provided that demonstrate good performance and a remarkable robustness of the algorithm (meaning by this a virtual impossibility to create a dead-lock in a random scene).     

Optimal motion planning for parallel robots via convex optimization and receding horizon

In this paper, the problem of motion planning for parallel robots in the presence of static and dynamic obstacles has been investigated. The proposed algorithm can be regarded as a synergy of convex optimization with discrete optimization and receding horizon. This algorithm has several advantages, including absence of trapping in local optimums and a high computational speed. This problem has been fully analyzed for two three-DOF parallel robots, ie 3s-RPR parallel mechanism and the so-called Tripteron, while the shortest path is selected as the objective function. It should be noted that the first case study is a parallel mechanism with complex singularity loci expression from a convex optimization problem standpoint, while the second case is a parallel manipulator for which each limb has two links, an issue which increases the complexity of the optimization problem. Since some of the constraints are non-convex, two approaches are introduced in order to convexify them: (1) A McCormick-based relaxation merged with a branch-and-prune algorithm to prevent it from becoming too loose and (2) a first-order approximation which linearizes the non-convex quadratic constraints. The computational time for the approaches presented in this paper is considerably low, which will pave the way for online applications.     

Robot Motion Planning: A Game-Theoretic Foundation

Analysis techniques and algorithms for basic path planning have become quite valuable in a variety of applications such as robotics, virtual prototyping, computer graphics, and computational biology. Yet, basic path planning represents a very restricted version of general motion planning problems often encountered in robotics. Many problems can involve complications such as sensing and model uncertainties, nonholonomy, dynamics, multiple robots and goals, optimality criteria, unpredictability, and nonstationarity, in addition to standard geometric workspace constraints. This paper proposes a unified, game-theoretic mathematical foundation upon which analysis and algorithms can be developed for this broader class of problems, and is inspired by the similar benefits that were obtained by using unified configuration-space concepts for basic path planning. By taking this approach, a general algorithm has been obtained for computing approximate optimal solutions to a broad class of motion planning problems, including those involving uncertainty in sensing and control, environment uncertainties, and the coordination of multiple robots. Received November 11, 1996; revised March 13, 1998.     

基于遗传算法的多机器人系统最优轨迹规划

针对关节型多机器人系统在静态环境下的点到点的轨迹规划问题,提出了一种基于遗传算法的最优轨迹规划策略.采用遗传算法在综合考虑各机器人沿轨迹运动的安全性、运动代价以及运动学约束的基础上为单个机器人规划最优的运动轨迹,并通过协调各机器人沿预定轨迹运行的时间避免机器人之间碰撞的发生.针对含有3个二自由度平面关节型机器人的多机器人系统进行了仿真实验,实验结果验证了该方法的有效性.     

基于模糊控制器的移动机器人路径规划仿真

目前移动机器人系统已经被广泛研究,机器人的避碰控制策略也多种多样.主要借鉴模糊控制的思想来解决移动机器人路径规划中的避碰问题,实现机器人在障碍物环境中快速、准确的找到一条无碰撞的路径,最终达到目标点.首先介绍了模糊控制的理论基础,然后对路径规划算法进行了推导,在总结经验建立模糊规则的基础上,运用模糊推理,构造出一张实践效果较好的控制响应表,仿真结果表明了该算法应用于移动机器人路径规划具有正确性、实用性和智能性等,该方法计算量小,运算速度快,提高了机器人控制的速度.