一种混联码垛机器人智能避障轨迹规划与仿真

由于码垛机器人应用环境复杂、不确定条件多等诸多问题,针对现有的避障轨迹规划算法存在的划分空间上的繁琐情况,提出了一种智能避障轨迹规划算法,包括障碍物信息提取和避障轨迹设计,最大程度降低障碍物信息与轨迹设计部分的耦合度,达到减少动态空间下频繁划分空间的目的.最后在OPENGL软件上研发的三维仿真平台上进行运动学仿真与验证.仿真结果表明,改进算法规划出的轨迹能有效避开障碍物且轨迹符合预期的要求,充分验证了轨迹规划算法的可行性和有效性.     

一种移动机器人的路径规划算法

本文提出一种移动机器人路径规划最短切线路径算法。依据此算法，机器人能顺利地避开障碍物到达目标位置，其原理简单，计算快捷，容易实现。仿真结果验证了它的有效性和实用性。     

足球机器人路径规划算法的研究及其仿真

研究足球机器人路径规划优化问题,足球机器人由于赛场情况千变万化,系统本身存在非线性,环境也具有时变性特点,要求机器人相互协作实时性要求高。结合足球机器人系统特点,提出一种蚁群算法的足球机器人路径规划算法。把每一只蚂蚁看作是一个机器人,蚂蚁根据信息素调整自己的前进方向,通过蚂蚁间的信息交流和相互协作快速找到一条最短的机器人运行无碰撞的路径。采用算法进行测试,结果表明,用蚁群算法较好地克服了局部最优的缺陷,获得最优路径,且无碰撞现象,符合足球机器人路径规划的实时性要求。     

移动机器人路径规划技术的现状与发展

移动机器人技术是近年来的研究热点,路径规划技术是移动机器人技术研究中的一个重要领域。路径规划分为基于模型的环境已知的全局路径规划和基于传感器的环境未知的局部路径规划。该文详细地叙述了移动机器人路径规划技术的分类和发展现状,全局路径规划和局部路径规划中的各种方法,具体地分析了各种方法的算法过程,并指出了各种方法的优缺点,以及各种方法的改进的办法,最后对移动机器人路径规划技术的未来的发展趋势进行了展望。     

多机器人系统中的动态避碰规划

研究冲突区域中多机器人间的协调和避碰问题。采用集中-分布相结合的规划方法，根据系统的拓扑结构为每个机器人规划路径，在冲突区域内使用优先级策略对机器人的运动特征进行分布式规划。通过上下层智能的融合，提高整个系统的智能。     

大数据支持下考虑障碍避让的机器人路径规划

为了使机器人在未知环境内可以成功规划出躲避障碍物的最短路径,在大数据支持下,设计了一种考虑障碍避让的机器人路径规划方法.首先分析机器人障碍物避让原理,确定机器人能够自由活动的范围.然后通过蚂蚁族群觅食流程对存在障碍物的路径进行模拟,获得基础路径规划结果,随后凭借赌轮盘规则细化,挑选节点通过机器人滚动窗口内映射,从而确定...     

基于模糊Elman网络算法的移动机器人路径规划分析

为了调正移动机器人避障线路,建立了基于模糊Elman网络算法的移动机器人路径规划模型,并应用进行Matlab仿真分析。利用现有障碍物的距离信息来实现机器人步长的实施可控制与调节,防止移动机器人在做出准确避障行为之后因为没有设定合适的步长而导致撞上障碍物,以0.5作为机器人的最初运动步长。仿真结果表明,采用模糊Elman网络可以获得比其它两种方法更优的路径规划效果,同时对障碍物进行高效避让,由此实现最优的路径规划。采用模糊Elman网络来构建得到的路径规划算法能够满足规划任务的要求,同时还能够根据机器人处于不同工作空间中的情况进行灵活调整。     

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。     

Extending obstacle avoidance methods through multiple parameter-space transformations

Obstacle avoidance methods approach the problem of mobile robot autonomous navigation by steering the robot in real-time according to the most recent sensor readings, being suitable to dynamic or unknown environments. However, real-time performance is commonly gained by ignoring the robot shape and some or all of its kinematic restrictions which may lead to poor navigation performance in many practical situations. In this paper we propose a framework where a kinematically constrained and any-shape robot is transformed in real-time into a free-flying point in a new space where well-known obstacle avoidance methods are applicable. Our contribution with this framework is twofold: the definition of generalized space transformations that cover most of the existing transformational approaches, and a reactive navigation system where multiple transformations can be applied concurrently in order to optimize robot motion decisions. As a result, these transformations allow existing obstacle avoidance methods to perform better detection of the surrounding free-space, through “sampling” the space with paths compatible with the robot kinematics. We illustrate how to design these space transformations with some examples from our experience with real robots navigating in indoor, cluttered, and dynamic scenarios. Also, we provide experimental results that demonstrate the advantages of our approach over previous methods when facing similar situations.

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.     

Heuristic collision-free path planning for an autonomous platform

In this paper, a heuristic and learning, algorithmic scheme for collision-free navigation is presented. This scheme determines an optimum collision-free navigation path of an autonomous platform by using a trial and error process, past navigation knowledge and current information extracted from the generated surrounding environment.     

Movement Planning in the Presence of Flows

This paper investigates the problem of time-optimum movement planning in two and three dimensions for a point robot which has bounded control velocity through a set of n polygonal regions of given translational flow velocities. This intriguing geometric problem has immediate applications to macro-scale motion planning for ships, submarines, and airplanes in the presence of significant flows of water or air. Also, it is a central motion planning problem for many of the meso-scale and micro-scale robots that have been constructed recently, that have environments with significant flows that affect their movement. In spite of these applications, there is very little literature on this problem, and prior work provided neither an upper bound on its computational complexity nor even a decision algorithm. It can easily be seen that an optimum path for the 2D version of this problem can consist of at least an exponential number of distinct segments through flow regions. We provide the first known computational complexity hardness result for the 3D version of this problem; we show the problem is PSPACE hard. We give the first known decision algorithm for the 2D flow path problem, but this decision algorithm has very high computational complexity. We also give the first known efficient approximation algorithms with bounded error.     

Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem

Kinematically redundant robots allow simultaneous execution of several tasks with different priorities. Beside the main task, obstacle avoidance is one commonly used subtask. The ability to avoid obstacles is especially important when the robot is working in a human environment. In this paper, we propose a novel control method for kinematically redundant robots, where we focus on a smooth, continuous transition between different tasks. The method is based on a new and very simple null-space formulation. Sufficient conditions for the tasks design are given using the Lyapunov-based stability discussion. The effectiveness of the proposed control method is demonstrated by simulation and on a real robot. Pros and cons of the proposed method and the comparison with other control methods are also discussed.     

Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots

Real time path planning for mobile robots requires fast convergence to optimal paths. Most rapid collision free path planning algorithms do not guarantee the optimality of the path. In this paper we present a Guided Autowave Pulse Coupled Neural Network (GAPCNN) approach for mobile robot path planning. The proposed model is a novel approach that improves upon the recently presented Modified PCNN (MPCNN) by introducing directional autowave control and accelerated firing of neurons based on a dynamic thresholding technique. Simulation studies and experimental results in both static as well as dynamic environments confirm GAPCNN to be a robust and time efficient path planning scheme for finding optimal paths.     

A technique for time-jerk optimal planning of robot trajectories

A technique for optimal trajectory planning of robot manipulators is presented in this paper. In order to get the optimal trajectory, an objective function composed of two terms is minimized: a first term proportional to the total execution time and another one proportional to the integral of the squared jerk (defined as the derivative of the acceleration) along the trajectory. This latter term ensures that the resulting trajectory is smooth enough. The proposed technique enables one to take into account kinematic constraints on the robot motion, expressed as upper bounds on the absolute values of velocity, acceleration and jerk. Moreover, it does not require the total execution time of the trajectory to be set a priori. The algorithm has been tested in simulation yielding good results, also in comparison with those provided by another important trajectory planning technique.     

Path planning with obstacle avoidance based on visibility binary tree algorithm

In this paper, a novel method for robot navigation in dynamic environments, referred to as visibility binary tree algorithm, is introduced. To plan the path of the robot, the algorithm relies on the construction of the set of all complete paths between robot and target taking into account inner and outer visible tangents between robot and circular obstacles. The paths are then used to create a visibility binary tree on top of which an algorithm for shortest path is run. The proposed algorithm is implemented on two simulation scenarios, one of them involving global knowledge of the environment, and the other based on local knowledge of the environment. The performance are compared with three different algorithms for path planning.     

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.     

Steer angle fields: An approach to robust manoeuvring in cluttered, unknown environments

In this paper a simple, robust and efficient method for local obstacle avoidance is described. It takes into account both the geometric and the kinematic properties of the robot in order to calculate allowed and forbidden steer angles.

The algorithm has been implemented and extensively tested on our mobile robot. Results have shown that the robot is able to operate robustly in unknown and uprepared in-door environments for extended periods.     

Mission design for a group of autonomous guided vehicles

In this paper, a generic approach for the integration of vehicle routing and scheduling and motion planning for a group of autonomous guided vehicles (AGVs) is proposed. The AGVs are requested to serve all the work stations in a two-dimensional environment while taking into account kinematics constraints and the environment’s geometry during their motion. The problem objective is the simultaneous determination of time-optimum and collision-free paths for all AGVs. The proposed method is investigated and discussed through a number of simulated experiments using a variety of environments and different initial conditions.