基于CPLD的彩色视觉移动机器人路径跟踪系统

路径跟踪是机器人视觉导航控制基本技术之一,为使机器人沿地面彩色引导线自主运动,并能在适时离线执行任务后自动返航,提出了一种用可编程逻辑器件(CPLD)实现的视觉伺服PID控制方法.该方法利用图像特征反馈对其所跟踪的路经进行实时识别跟踪.仿真结果表明,该方法改善了控制算法的实时性,提高了移动机器人的路径跟踪精度与速度.     

The Approximate Cell Decomposition with Local Node Refinement Global Path Planning Method: Path Nodes Refinement and Curve Parametric Interpolation

The paper presents a novel global path planning approach for mobile robot navigation in two dimensional workspace cluttered by polygonal obstacles. The core of the planning method introduced is based on the approximate cell decomposition method. The advantage of the new method is the employment of novel path refinement procedures of the paths produced by approximate cell decomposition that are based on local characteriscics of the workspace. Furthermore, the refined path is parametrically interpolated by cubic splines via a physical centripetal model, introducing the dynamic constraints of mobile robots' motion to the path construction. The method has been implemented both in a computer graphics simulation and on a real mobile robot cruising at indoor environments. Planned paths on several configurations are presented.     

实现机器人动态路径规划的仿真系统

针对机器人动态路径规划问题,提出了在动态环境中移动机器人的一种路径规划方法,适用于环境中同时存在已知和未知,静止和运动障碍物的复杂情况。采用栅格法建立机器人空间模型,整个系统由全局路径规划和局部避碰规划两部分组成。在全局路径规划中,用快速搜索随机树算法规划出初步全局优化路径,局部避碰规划是在全局优化路径的同时,通过基于滚动窗口的环境探测和碰撞规则,对动态障碍物实施有效的局部避碰策略,从而使机器人安全顺利地到达目的地。仿真实验结果说明该方法具有可行性。     

A bayesian approach to real-time obstacle avoidance for a mobile robot

Real-time obstacle avoidance is essential for the safe operation of mobile robots in a dynamically changing environment. This paper investigates how an industrial mobile robot can respond to unexpected static obstacles while following a path planned by a global path planner. The obstacle avoidance problem is formulated using decision theory to determine an optimal response based on inaccurate sensor data. The optimal decision rule minimises the Bayes risk by trading between a sidestep maneuver and backtracking to follow an alternative path. Real-time implementation is emphasised here as part of a framework for real world applications. It has been successfully implemented both in simulation and in reality using a mobile robot.     

一种基于连接机制和时空经验的认知地图学习与导航方法

提出了一种连接主义方法, 利用移动机器人自身的时空经验, 在缺乏全局坐标信息和环境先验模型的情况下, 建立面向目标的认知地图. 在线形成的时序处理网络 (TSPN)可提供简洁的历史感知信息, 以神经元激活特性保存空间知识, 引导机器人运动. 结合TSPN和反应式行为模块的导航系统可实现动态的路标及方向检测、路径学习和实时导航功能. 仿真和实际实验验证了系统的有效性和适应性.     

基于路径积分强化学习方法的蛇形机器人目标导向运动

路径积分方法源于随机最优控制,是一种数值迭代方法,可求解连续非线性系统的最优控制问题,不依赖于系统模型,快速收敛.文中将基于路径积分强化学习的策略改善方法用于蛇形机器人的目标导向运动.使用路径积分强化学习方法学习蛇形机器人步态方程的参数,不仅可以在仿真环境下使蛇形机器人规避障碍到达目标点,利用仿真环境的先验知识也能在实际环境下快速完成相同的任务.实验结果验证方法的正确性.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

基于粒子滤波的一种全局路径规划方法

提出一种基于粒子滤波的全局路径规划方法, 以多段Ferguson样条曲线表示路径确保所得路径光滑且一阶连续. 将最优路径视为真实状态, 将其他路径视为受噪声污染的状态, 从而将最优路径的搜索过程视为状态空间中对真实状态的滤波过程. 以粒子滤波算法依据路径评价函数对状态空间实施滤波获得最优路径, 仿真结果表明该方法实现简单、收敛迅速、且所得到路径光滑, 易于为机器人直接采用.     

Computing collision-free motions for a team of robots using formation and non-holonomic constraints

This paper focuses on the collision-free motion of a team of robots moving in a 2D environment with formation and non-holonomic constraints. With the proposed approach one can simultaneously control the formation of the team and generate a safe path for each individual robot. The computed paths satisfy the non-holonomic constraints, avoid collisions, and minimize the task-completion time. The proposed approach, which combines techniques from mathematical programming and CAD, consists of two main steps: first, a global team path is computed and, second, individual motions are determined for each unit. The effectiveness of the proposed approach is demonstrated using several simulation experiments.     

基于改进动态窗口法的户外清扫机器人局部路径规划

针对清扫机器人在停车场结构化路面下存在的加速度过大、路径偏离全局路径过大等问题,提出了一种改进的动态窗口法（DWA）．首先,为了限制小车加速度的范围,对DWA速度空间的动力学约束进行优化,避免出现过大的加速度导致轮胎垂直载荷过小的状况．然后,基于激光里程计对轨迹推算环节进行实时误差补偿,解决停车场路面下路径偏离全局路径较大的问题．最后将改进的DWA应用于四轮独立驱动、独立转向的清扫机器人上进行对比实验．实验结果表明,在相同的全局路径、相同的路况下,改进DWA的路径平均误差较传统DWA减小了约60%,轮胎垂直载荷在不同路况下也大大提高,验证了本文方法的有效性和可靠性．     

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.     

Strategy of approach for seizure of an assistive mobile manipulator

In assistive robotics, a manipulator arm constitutes one possible solution for restoring some manipulation functions to victims of upper limb disabilities. The aim of this paper is to present a global strategy of approach of an assistive mobile manipulator (manipulator arm mounted on a mobile base). A manipulability criterion is defined to deal with the redundancy of the system. The aim is to keep the arm manipulable, i.e. capable of moving by itself. The strategy is based on human-like behaviour to help the disabled operator to understand the action of the robot. When the robot is far from its objective, only the mobile base moves, thus avoiding obstacles if necessary. When the objective is close to the robot, both mobile base and arm move and redundancy can be used to maximise a manipulability criterion. All the situations are tested separately and a global mission is realised in which all the previous situations are encountered. The partial results obtained with the real robot consolidate the results of simulation. This paper does not propose an autonomous path planning and navigation of the mobile arm but an assistance to the user for remote controlling it.     

An Optimal Control-Based Formulation to Determine Natural Locomotor Paths for Humanoid Robots

In this paper we explore the underlying principles of natural locomotion path generation of human beings. The knowledge of these principles is useful to implement biologically inspired path planning algorithms on a humanoid robot. By 'locomotion path' we denote the motion of the robot as a whole in the plane. The key to our approach is to formulate the path planning problem as an optimal control problem. We propose a single dynamic model valid for all situations, which includes both non-holonomic and holonomic modes of locomotion, as well as an appropriately designed unified objective function. The choice between holonomic and non-holonomic behavior is not accomplished by a switching model, but it appears in a smooth way, along with the optimal path, as a result of the optimization by efficient numerical techniques. The proposed model and objective function are successfully tested in six different locomotion scenarios. The resulting paths are implemented on the HRP-2 robot in the simulation environment OpenHRP as well as in the experiment on the real robot.     

Sensor-based path planning for nonholonomic mobile robots subject to dynamic constraints

It is generally not easy to achieve smooth path planning in an unknown environment for nonholonomic mobile robots, which are subject to various robot constraints. In this paper, a hybrid approach is proposed for smooth path planning with global convergence for differential drive nonholonomic robots. We first investigate the use of a polar polynomial curve (PPC) to produce a path changing continuously in curvature and satisfying dynamic constraints. In order to achieve path generation in real-time, a computationally effective method is proposed for collision test of the complex curve. Then, a hybrid path planning approach is presented to guide the robot to move forward along the boundary of an obstacle of arbitrary shape, by generating a proper “Instant Goal” (and a series of deliberate motions through PPC curve based path generation) and planning reactively when needed using a fuzzy controller for wall following. The choice of an Instant Goal is limited to the set of candidates that are practically reachable by the robot and that enable the robot to continue following the obstacle. The effectiveness of the proposed approach is verified by simulation experiments.     

融合改进A*算法和Morphin算法的移动机器人动态路径规划

在动态未知环境下对机器人进行路径规划,传统A*算法可能出现碰撞或者路径规划失败问题。为了满足移动机器人全局路径规划最优和实时避障的需求,提出一种改进A*算法与Morphin搜索树算法相结合的动态路径规划方法。首先通过改进A*算法减少路径规划过程中关键节点的选取,在规划出一条全局较优路径的同时对路径平滑处理。然后基于移动机器人传感器采集的局部信息,利用Morphin搜索树算法对全局路径进行动态的局部规划,确保更好的全局路径的基础上,实时避开障碍物行驶到目标点。MATLAB仿真实验结果表明,提出的动态路径规划方法在时间和路径上得到提升,在优化全局路径规划的基础上修正局部路径,实现动态避障提高机器人达到目标点的效率。     

基于JPS策略的ACS移动机器人全局路径规划

针对蚁群系统（ACS）算法收敛速度慢、易陷入局部最优、路径转折点数量过多等问题,提出了一种基于跳点搜索（JPS）策略的ACS全局路径规划算法．该算法在迭代前加入一只特殊蚂蚁,利用方向因子引导该蚂蚁始终朝着目标方向前进,并查询是否存在最简路径;在蚂蚁查询下一个节点时,利用JPS算法思想舍去大部分不需要计算的节点．最后,为验证该方法的有效性,使用不同规格的栅格地图进行了仿真实验,仿真结果表明,改进的ACS算法相比于ACS算法,收敛速度加快、收敛时间缩短,且路径更优．最后将算法应用到实际的基于机器人操作系统（ROS）的移动机器人导航实验中,实验结果表明,改进的ACS算法能够有效地解决移动机器人全局路径规划问题,且能明显提升机器人全局路径规划的效率．     

A virtual target approach for resolving the limit cycle problem in navigation of a fuzzy behaviour-based mobile robot

A virtual target approach is proposed for resolving the limit cycle problem in navigation of a behaviour-based mobile robot. Starting from the onset point of a possible limit cycle path, the real target is switched to a virtual location and the robot is navigated according to the virtual target set up temporarily and the real environment information sensed, until a switching-back condition is reached. The cause of the limit cycle is analysed and the abrupt change in target orientations at two consecutive reaction instants is then identified to be the condition for shifting the target from the real location to the virtual one. The condition for switching back to the real target is established using a specific change in the obstacle information sensed. The algorithm is described together with some particular considerations in implementation. Efficiency and effectiveness of the proposed approach are verified through simulation and experiments conducted with a Nomad 200 robot incorporating a fuzzy behaviour-based controller.     

一种改进的未知动态环境下机器人混合路径规划方法

动态未知环境下的机器人路径规划是机器人导航领域的重要课题之一,采用传统的方法求解并不理想。针对这个问题,提出一种改进的机器人混合路径规划方法。首先利用改进的文化基因算法规划出较优的全局路径,指引机器人沿着全局路径行走,然后根据传感器探测到的局部环境信息,利用Morphin算法进行局部路径实时规划,使机器人有效地躲避动态障碍物。仿真实验表明,该算法在未知动态路径规划中具有良好的效果。     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.     

基于改进粒子群算法的机器人路径规划方法

提出一种基于粒子群算法的机器人路径规划方法.将路径规划看作一个带约束的优化问题,约束条件为路径不能经过障碍物,优化目标为整个路径的长度最短.机器人工作空间中的障碍物描述为多边型,对障碍物的顶点进行编号.利用粒子群算法进行路径规划,每一个粒子定义为一个由零或障碍物顶点编号组成的集合,在粒子的迭代过程中考虑约束条件,惯性权重随迭代次数动态改变,使算法既有全局搜索能力也有较强的局部搜索能力.仿真结果表明该方法的正确性和有效性.