基于神经网络的机器人路径规划算法

机器人路径规划技术是机器人研究的一个重要领域.针对未知的全局环境,使机器路径最优化,利用机器人传感器网络建立可视区域,将整体任务分解为环境信息已知的一系列子任务,利用神经网络高速并行计算的优点,建立神经网络罚函数,提出一种实时性较高的变参数方法离散化求取罚函数的负梯度方向,控制机器人快速高效地完成子任务,从而驱使机器人到达目标点并进行仿真.仿真结果证明了复杂环境静态和动态目标指引下方法的有效性和实用性,特别适用于实时性要求高的场合.     

足球机器人路径规划的 PSO 方法

研究足球机器人在已知静态环境下路径规划问题,在避障环境下寻求最优路径,提出了一种基于粒子群优化算法的足球机器人路径规划方法.为适应 PSO 算法的自身特点和提高算法搜索的效率,在传统栅格法的基础上引入实际坐标系法,对环境进行建模;为了更好地评价粒子(即解)的性能,在进行碰撞判定的基础之上,引入罚函数方法,克服了传统适应度函数难以更好地表达粒子性能的缺点.进行仿真的结果表明,该算法在足球机器人路径规划方面具有可行性、有效性和鲁棒性.     

足球机器人路径规划的PSO方法

研究足球机器人在已知静态环境下路径规划问题,在避障环境下寻求最优路径,提出了一种基于粒子群优化算法的足球机器人路径规划方法。为适应PSO算法的自身特点和提高算法搜索的效率,在传统栅格法的基础上引入实际坐标系法,对环境进行建模;为了更好地评价粒子（即解）的性能,在进行碰撞判定的基础之上,引入罚函数方法,克服了传统适应度函数难以更好地表达粒子性能的缺点。进行仿真的结果表明,该算法在足球机器人路径规划方面具有可行性、有效性和鲁棒性。     

基于改进人工势场模型的移动机器人路径规划

针对传统人工势场模型在移动机器人路径规划中存在局部极值的问题,提出了一种改进方法。该方法首先将传统势场模型转换成解空间中寻优的问题,再加入罚因子建立罚函数数学模型。新的势场模型能够有效的使得机器人成功逃逸局部极值点。最后通过MATLAB进行仿真实验,仿真实验结果证明该方法的有效性。     

神经网络在机器人路径规划中的应用研究

机器人路径规划是人工神经网络应用于机器人控制的重要内容 .本文提出采用碰撞罚函数进行无碰撞轨迹规划 ,给出了无碰撞轨迹规划的人工神经网络算法 ,并进行了计算机仿真     

基于云模型的粒子群优化算法在路径规划中的应用

利用罚函数将机器人路径规划有约束优化问题转换为无约束优化问题。利用云模型既有随机性又有稳定倾向性的特性,引入基于云模型理论的自适应参数策略,构造出一种改进的粒子群（PSO）算法,并应用于机器人路径规划问题。在不同的子群采用不同的惯性权重生成方法,有效地平衡了算法的局部和全局搜索能力,提高了种群的多样性和算法的收敛速度。仿真结果对比验证了该算法的可行性和有效性,且实现简单、收敛速度快。     

基于改进遗传算法的机器人路径规划与仿真

针对标准遗传算法解决机器人处于障碍环境下寻找最优路径局部寻优精度较差、规划效率低的问题,提出一种改进遗传算法的机器人路径规划方法。该算法采用一维编码表示路径,构造了路径最优化的目标函数和适应度函数,利用多个种群拓宽搜索空间,提高了规划效率,采用保优选择策略,避免陷入局部最优。仿真结果表明,改进遗传算法比标准遗传算法路径规划质量高,能够获得平滑的低代价路径,稳定性好,是机器人路径规划的一种较好的方法,且具有一定的推广意义。     

基于环境分层方法的双足机器人路径规划研究

为了实现双足机器人在障碍环境中的路径规划,提出一种将三维环境分层的方法,用两个截面将环境分为高于机器人身高障碍层、低于机器人抬脚高度障碍层和中间障碍层.首先在中间障碍层进行机器人轨迹规划,再根据机器人各种步态的不同损耗构建代价函数,把规划好的轨迹放到最底层进行规划修改,最终得到双足机器人在规划路径上代价最小的一系列连续的动作,通过计算机仿真实验验证了方法的有效性.     

一种改进PSO的室内机器人路径规划方法

针对室内空间局限性造成的移动机器人路径规划难度提升问题,文章分析了机器人室内移动中转弯、启停等运动特征,为获得最优规划路径引入了粒子群算法(particle swarm optimization, PSO),同时为改善经典算法中收敛度低,易早熟等问题,首先使用收敛因子、线性递减、非线性凹函数、随机分布方式等对PSO惯性权重的选取进行了讨论,并结合三次样条插值方法、选取罚函数作为适应度函数等对PSO进行了算法改进,最后,以实验室作为室内环境背景进行了仿真实验,并与经典的PSO路径规划方法进行了对比,实验结果表明,文章中改进的PSO路径规划方法精度高于经典PSO方法5%,平均寻优时间比经典PSO的少5s左右,能够有效的提高规划路径的平滑度,对于室内环境中机器人路径规划具有良好的实时性和有效性。     

基于遗传算法的变电站巡检机器人任务路径规划方法研究

近年来,随着变电站巡检机器人在变电站中的广泛使用,巡检机器人路径规划问题越来越成为亟待解决的问题。巡检机器人在已知的拓扑地图中标记了待执行巡检任务的停靠点,不同任务需要从初始点出发经过不同的一系列停靠点再返回初始点,如何规划路径是机器人面临的问题。首先分析了路径规划面临的问题,然后通过分析拓扑地图的特征,对地图进行等价简化,再对问题进行建模使用遗传算法求解巡检任务路径规划的近似最优解。通过仿真实验证明,提出的基于遗传算法的路径规划方法是可行有效的,为变电站巡检机器人任务路径规划提供了一种有效方法。     

动态仓储环境下的多机器人路径规划方法

针对动态仓储环境下多机器人运动过程中出现的拥塞死锁问题,利用路径长度、转弯数、路径惩罚函数建立小车单任务耗时模型。模型引入阻塞惩罚函数,移除可能发生阻塞的路径增加罚值。同时针对传统遗传算法路径规划操作过程中路径交叉变异导致路径中断不可用的情况,设计重复点交叉算子,在变异操作后检查路径合法性,使算法都是在可行的解空间上进行搜索。仿真实验表明,算法能指导机器人获得动态环境下的最优路径,同时算法收敛速度大大提高。     

Collision avoidance for Robot manipulators: Application to Catia/IBM 7565 interface

A collision avoidance algorithm has been developed to augment the capability of an automatic (off-line) robot path planning (programming) tool. The use of off-line programming tools for robot task programming is becoming increasingly important, but the advantages to be gained by off-line programming may be lost if collision-free path planning capabilities are not included. This article addressed the problem of collision-free path planning in the context of a gantry type robot. The collision avoidance algorithm described here uses the <heuristic approach> to collision-free path planning. The manipulator and obstacles are modeled as spheres to simplify tests for collision. An important feature of this algorithm is that it permits the manipulation of objects in the robot's environment. When compared against an algorithm from the literature, given a lightly cluttered environment modelled by spheres, the new algorithm finds a collision-free path much faster. This new algorithm has been implemented as part of the CATIA/IBM 7565 interface which forms an automatic off-line programming system for the IBM 7565 robot. It has also been implemented as a supervisory collision filter to allow collision-free control of the robot from the operator's console. In both cases the algorithm has been demonstrated to provide efficient and effective collision avoidance for the IBM 7565 robot.     

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.     

一种基于改进冲突搜索的多机器人路径规划算法

针对智能仓储环境下多载位自主移动机器人集群拣选-配送路径规划问题,提出一种改进型基于冲突搜索的多智能体路径规划算法.在模型方面,采用多载位机器人替代KIVA机器人,建立以最小化拣选-配送时间以及无效路径比为目标的数学规划模型.在算法方面,首先,提出一种基于优先级规则的多智能体冲突消解加速策略;然后,设计基于动态规划的单机器人拣选序列优化算法;最后,设计考虑转向惩罚的增强A*算法搜索机器人最优路径.实验结果表明:所提出模型与KIVA系统相比有较大优越性;所提出算法能够有效缩短拣选-配送时间、减少无效路径时间.     

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.     

Path planning for a tracked robot traversing uneven terrains based on tip-over stability

In the real-world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability-based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high-stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability-based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg–Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability-based path planning framework can effectively plan the high-quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.     

基于遗传算法的移动机器人动态路径规划研究

针对移动机器人未知、动态环境下路径规划的难题,对移动机器人进行了系统设计,采用动态栅格法对环境建模,在对传统遗传算法进行一定的改进的基础上,个体评价函数采取可行路径适应度函数和不可行路径适应度函数分别进行处理,通过算法设计和仿真可知,采用该方法对移动机器人进行动态路径规划时,与任何障碍物不发生碰撞,路径短而且规划曲线平滑,达到了满意的规划效果和收敛速度。     

一种改进的全覆盖路径规划算法

全覆盖路径规划在现实生活中具有很广泛的应用,本文针对已存在的全覆盖路径规划算法中的内螺旋算法进行改进,提出带有优先级的内螺旋算法PISC算法。在算法中加入行走优先级,并采用回溯法解决清扫机器人进入的死角问题,优化机器人的清扫路径,最后在Visual C+〖KG-*3〗+6.0编程环境下进行算法仿真。实验结果表明,清扫机器人能有效地避开障碍物,在自由区域顺利行走,提高了清扫机器人的清扫效率,减少了机器人清扫的重复路径。     

LP-based velocity profile generation for robotic manipulators

In this paper we examine the minimum-time velocity profile generation problem which belongs to the second stage of the decoupled robot motion planning. The time-optimal profile generation problem can be translated to a convex optimal control task through a nonlinear change of variables. When the constraints of the problem have special structure, the time-optimal solution can be obtained by linear programming (LP). In this special case, the velocity of the robot along the path is maximised, instead of time minimising. The benefit of the LP solution is the lower computational time. Validation of the LP algorithm is also presented based on simulation results.     

罚函数凸优化迭代算法及在无人机路径规划中的应用

针对无人机路径规划问题,建立了具有定常非线性系统、非仿射等式约束、非凸不等式约束的非凸控制问题模型,并对该模型进行了算法设计和求解。基于迭代寻优的求解思路,提出了凸优化迭代求解方法和罚函数优化策略。前者利用凹凸过程(CCCP)和泰勒公式对模型进行凸化处理,后者将经处理项作为惩罚项施加到目标函数中以解决初始点可行性限制。经证明该方法严格收敛到原问题的Karush-Kuhn-Tucker(KKT)点。仿真实验验证了罚函数凸优化迭代算法的可行性和优越性,表明该算法能够为无人机规划出一条满足条件的飞行路径。