Complete coverage path planning for wheeled agricultural robots

In the agricultural industry, an evolutionary effort has been made over the last two decades to achieve precise autonomous systems to perform typical in-field tasks, including harvesting, mowing, and spraying. One of the main objectives of an autonomous system in agriculture is to improve the efficiency while reducing the environmental impact and cost. Due to the nature of these operations, complete coverage path planning (CCPP) approaches play an essential role to find an optimal path which covers the entire field while taking into account land topography, operation requirements, and robot characteristics. The aim of this paper is to propose a CCPP approach defining the optimal movements of mobile robots over an agricultural field. First, a method based on tree exploration is proposed to find all potential solutions satisfying some predefined constraints. Second, a similarity check and selection of optimal solutions method is proposed to eliminate similar solutions and find the best solutions. The optimization goals are to maximize the coverage area and to minimize overlaps, nonworking path length, and overall travel time. To explore a wide range of possible solutions, our approach is able to consider multiple entrances for the robot. For fields with a complex shape, different dividing lines to split them into simple polygons are also considered. Our approach also computes the headland zones and covers them automatically which leads to a high coverage rate of the field.     

Lifting path planning of mobile cranes based on an improved RRT algorithm

Lifting operations of mobile cranes are one of the commonly-seen and most important activities for prefabrication housing production (PHP) on sites. However, relevant operations are normally based on the experience of operators or project managers, this often leads to low efficiency as well as high accident rate due to dynamic and complex construction sites. Thus, it is important and necessary to develop an appropriate approach to the lifting planning of mobile cranes so as to guide on-site operations. This paper proposes an improved Rapidly-exploring Random Tree (RRT) algorithm for lifting path planning of mobile cranes. Considering the critical role of Nearest Neighbor Search (NNS) in the implementation of RRT algorithm, a novel strategy for searching the nearest neighbor is developed, i.e., Generalized Distance Method and Cell Method. Both methods are tested in simulation-based experiments. The results show that 1) the Generalized distance method not only reduces the search time, but also unifies the unit of distance measurement and clarifies the physical meaning of distance; 2) the Cell method dramatically reduces the traversal range as well as the search time; and 3) both methods improve the quality of lifting path planning of mobile cranes. This improved RRT algorithm enables rapid path planning of mobile cranes in a dynamic and complex construction environment. The outcomes of this research not only contribute to the body of knowledge in spatial path planning of crane lifting operations, but also have the potential of significantly improving efficiency and safety in crane lifting practices.     

基于传感器信息的智能移动机器人导航评述

导航是研究智能移动机器人技术中的一个重要领域,对自主导航技术的关键问题——路径规划进行了评述。路径规划一般可分为基于模型的环境信息完全知道的全局路径规划和基于传感器的环境信息完全未知或部分未知的局部路径规划2种类型。分别对各种方法的发展现状进行了总结,指出了各种方法的优点和不足。     

Path Planning for Autonomous Mobile Robots Using the Boundary Integral Equation Method

Path planning is a fundamental problem in mobile robotics. The objective of path planning is to find a sequence of states that carries a robot from some start state to the goal while avoiding obstacles. This paper focuses on point-to-point path planning, ie., developing a path between given points in a known environment with obstacles. A novel environment representation method is introduced which utilizes the Boundary Integral Equation (BIE) method to map the environment into continuous Harmonic potential fields. Different mappings and mechanisms for utilizing these mappings for path planning are developed, and a collection of path planning algorithms are described which exploit the properties of Harmonic fields and the computational efficiency of the BIE method.     

Coverage path planning on three‐dimensional terrain for arable farming

Field operations should be done in a manner that minimizes time and travels over the field surface and is coordinated with topographic land features. Automated path planning can help to find the best coverage path so that the field operation costs can be minimized. Intelligent algorithms are desired for both two‐dimensional (2D) and three‐dimensional (3D) terrain field coverage path planning. The algorithm of generating an optimized full coverage pattern for a given 2D planar field by using boustrophedon paths has been investigated and reported before. However, a great proportion of farms have rolling terrains, which have a considerable influence on the design of coverage paths. Coverage path planning in 3D space has a great potential to further optimize field operations. This work addressed four critical tasks: terrain modeling and representation, coverage cost analysis, terrain decomposition, and the development of optimized path searching algorithm. The developed algorithms and methods have been successfully implemented and tested using 3D terrain maps of farm fields with various topographic features. Each field was decomposed into subregions based on its terrain features. A recommended “seed curve” based on a customized cost function was searched for each subregion, and parallel coverage paths were generated by offsetting the found “seed curve” toward its two sides until the whole region was completely covered. Compared with the 2D planning results, the experimental results of 3D coverage path planning showed its superiority in reducing both headland turning cost and soil erosion cost. On the tested fields, on average the 3D planning algorithm saved 10.3% on headland turning cost, 24.7% on soil erosion cost, 81.2% on skipped area cost, and 22.0% on the weighted sum of these costs, where their corresponding weights were 1, 1, and 0.5, respectively. © 2011 Wiley Periodicals, Inc.     

动态环境中基于遗传算法的移动机器人路径规划的方法

动态环境中，移动机器人的动态路径规划是一个较难解决的课题．本文提出一种 基于遗传算法的移动机器人的路径规划方法．该方法采用实数编码的方法，有明确物理意义 的适应度函数，以加快实时的运算速度和提高运算精度．该方法充分挖掘可应用遗传算法解 决移动机器人动态路径规划的潜力．通过计算机仿真表明该控制方法具有良好的动态路径规 划能力．     

智能仿生算法在移动机器人路径规划优化中的应用综述

随着移动机器人应用领域的扩大和工作环境的复杂化,传统路径规划算法因其自身局限性变得难以满足人们的要求。近年来,智能仿生算法因其群集智慧和生物择优特性而被广泛应用于移动机器人路径规划优化中。首先,按照智能仿生算法仿生机制的来源,对应用于路径规划优化中的智能仿生算法进行了分类。然后,按照不同的类别,系统的叙述了各种新型智能仿生算法在路径规划优化中取得的最新研究成果,总结了路径规划优化过程中存在的问题以及解决方案,并对算法在路径规划优化中的性能进行了比较分析。最后对智能仿生算法在路径规划优化中的研究方向进行了探讨。     

A Sensor Placement Algorithm for a Mobile Robot Inspection Planning

In this paper, we address the inspection planning problem to ??see?? the whole area of the given workspace by a mobile robot. The problem is decoupled into the sensor placement problem and the multi-goal path planning problem to visit found sensing locations. However the decoupled approach provides a feasible solution, its overall quality can be poor, because the sub-problems are solved independently. We propose a new randomized approach that considers the path planning problem during solution process of the sensor placement problem. The proposed algorithm is based on a guiding of the randomization process according to prior knowledge about the environment. The algorithm is compared with two algorithms already used in the inspection planning. Performance of the algorithms is evaluated in several real environments and for a set of visibility ranges. The proposed algorithm provides better solutions in both evaluated criterions: a number of sensing locations and a length of the inspection path.     

基于支持向量机的移动Agent迁移策略

迁移策略为移动Agent规划出最佳迁移路径,保证移动Agent正确、高效地完成任务。文章分析了现有移动Agent系统中采用的几种代表性迁移策略,提出了基于支持向量机的移动Agent迁移策略。模拟实验结果表明,与其它迁移策略相比该策略能够以较大的概率得到最优的结果,从而验证了算法的有效性和可行性。     

面向平层多房间的内墙作业移动机器人路径规划

本文针对多房间的移动机器人内墙作业的路径规划任务,提出一种两阶段路径规划方法.第1阶段针对沿墙作业过程中环境存在灰尘或雾气造成的传感器失效问题,以及房间多出口时路径规划不完整问题,我们提出起点自动选择沿墙路径规划方法,基于栅格地图离线生成沿墙规划路径.第2阶段,针对点到点路径规划过程中的动态避障问题,我们提出一种基于PSAC (prioritized experience replay soft actor critic)算法的点到点路径规划方法,在软行动者-评论家(soft actor critic, SAC)的中引入优先级经验回放策略,实现机器人的动态避障.实验部分设计了沿墙路径规划对比实验和动态避障的对比实验,验证本文所提出的方法在室内沿墙路径规划和点到点路径规划的有效性.     

一种新的移动机器人环境模型

环境建模是移动机器人路径规划的基础．针对部分 已知或完全未知的环境,本文提出一种基于位置码的四叉树环境模型,通过建立四叉树节点 位置码与其笛卡尔坐标的一一对应关系,将对四叉树的操作转换到位置码空间,从而简化了 对四叉树的操作．文中研究了基于位置码的四叉树的重要操作,并给出了详细的算法;基于 该模型,作者利用A*算法进行路径规划的仿真研究,仿真结果证明了该模型的有效性．     

Coverage path planning algorithms for agricultural field machines

In this article, a coverage path planning problem is discussed in the case of agricultural fields and agricultural machines. Methods and algorithms to solve this problem are developed. These algorithms are applicable to both robots and human‐driven machines. The necessary condition is to cover the whole field, and the goal is to find as efficient a route as possible. As yet, there is no universal algorithm or method capable of solving the problem in all cases. Two new approaches to solve the coverage path planning problem in the case of agricultural fields and agricultural machines are presented for consideration. Both of them are greedy algorithms. In the first algorithm the view is from on top of the field, and the goal is to split a single field plot into subfields that are simple to drive or operate. This algorithm utilizes a trapezoidal decomposition algorithm, and a search is developed of the best driving direction and selection of subfields. This article also presents other practical aspects that are taken into account, such as underdrainage and laying headlands. The second algorithm is also an incremental algorithm, but the path is planned on the basis of the machine's current state and the search is on the next swath instead of the next subfield. There are advantages and disadvantages with both algorithms, neither of them solving the problem of coverage path planning problem optimally. Nevertheless, the developed algorithms are remarkable steps toward finding a way to solve the coverage path planning problem with nonomnidirectional vehicles and taking into consideration agricultural aspects. © 2009 Wiley Periodicals, Inc.     

基于QPSO 算法移动机器人轨迹规划与实验

针对移动机器人路径规划问题,提出一种基于QPSO算法的路径规划方法,并用概率论的方法分析了移动机器人路径规划的收敛性,阐明了该方法随均匀分布和正态分布的参数关系和收敛区间;然后根据移动机器人的运动特征提出一种改进的轨迹规划方法。移动机器人平台的实验结果表明了该方法在移动机器人路径规划中的有效性和可行性。     

基于嵌入式Linux的移动机器人控制系统

以嵌入式Linux开发平台为基础,根据模糊控制算法进行机器人路径规划,同时将两自由度云台和超声波测距模块相结合,扩大了障碍物检测范围。根据测试,移动机器人可以根据操作人员的语音指令,实现前进、后退、测量距离等动作,并通过MP3播放功能播放出所测量到的距离值和机器人当前的运行状态。在运行过程中,机器人可以自动检测方位并修正,实现无碰撞行驶。     

一种移动机器人全局最优路径规划算法

针对移动机器人全局最优路径规划问题,首先提出一种基于线性操作的遍历式算法,该算法通过场扫描方式生成步长转换矩阵(STM,Step Transform Matrix),并在步长转换矩阵中搜索考虑方向一致的最短路径,从而可以得到避免不必要路径转折的全局最短路径.其次提出了一种评价标准来区分路径优劣.最后通过仿真与圆形波传播算法进行了路径规划对比实验,实验结果表明本文所提算法在所提出的路径评价标准下可以获得比波传播算法更优的路径.     

Task apportionment in a rearrangement problem of multiple mobile robots

A rearrangement problem involving multiple mobile robots is addressed in this paper. In the problem, it is important to identify task decomposition, task allocation, and path planning applicable to distinct environments while rearrangement tasks are executed. We here define ‘task apportionment’ as an operation that sets up task decomposition and conducts task allocation based on that setup. We propose a method for task apportionment and path planning applicable to distinct environments. The method establishes the necessary intermediate configurations of objects as one way of task decomposition and determines task allocation and path planning as a semi-optimized solution by using simulated annealing. The proposed method is compared with a continuous transportation method and a territorial method through simulations and experiments. In the simulations, the proposed method is, on the average, 17 and 20% faster than the continuous transportation method and the territorial method, respectively. In the experiments, the proposed method is, on the average, 22 and 16% faster than the continuous transportation and the territorial method, respectively. These results show that the proposed method can realize an efficient rearrangement task by mobile robots in various working environments under feasible computation time, especially in environments with a mixture of wide and narrow areas and an uneven distribution of objects.     

No-overshoot control of robotic manipulators in the presence of obstacles

The long standing problem of guiding robotic manipulators in cluttered workspaces is treated here by a unified approach, taking into account both motion planning and control aspects. Planning the collision-free operation is based on the notion of roadmaps. To reduce the complexity of the roadmaps, the real problem is approximated by a simplified but still realistic graph structure. Motion planning relies on the robot closely following the defined collison-free path, so the errors in robot control must be contained within bounded regions. Satisfying this assumption ideally requires no-overshoot control. Overshooting demands special attention in tasks that require high speed operations of robots in the presence of obstacles. To stabilize at the specified collision-free trajectory, before going actually beyond it, joint control schemes demand slow movements through a number of intermediate subgoals. This significantly delays the completion of the task. By using ideas from variable structure control (VSC), a control policy is suggested here that switches between PD⁺ and CEA⁺ (constant error acceleration) schemes, where the CEA + scheme applies a “constant error acceleration” of the largest possible magnitude in the appropriate direction. The performance of the path planning and the control algorithms is presented through simulations of a four-link SCARA robot in various cluttered workspaces. © 1994 John Wiley & Sons, Inc.     

基于拓扑高程模型的室外三维环境建模与路径规划

针对复杂室外场景, 提出一种基于拓扑高程模型的三维环境建模方法. 采用自适应可变阈值聚类算法, 将映射到二维水平栅格中的激光点云划分为垂直单元和水平单元, 可实现三维场景中悬空环境特征的有效表述. 在此基础上对垂直单元进行高度离散采样, 从而构建与其相对应的拓扑结构, 并结合BOW (Bag of words)模型对室外三维环境中的典型景物进行辨识. 采用面向拓扑结构和高程图单元的分级匹配策略, 实现不同场景间的精确匹配, 构建具有全局一致性的拓扑高程地图. 利用辨识出来的环境特征和高程地图产生双重环境约束, 实现与室外地形相适应的自主路径规划. 实验结果和数据分析证明了本文环境建模与路径规划方法的有效性和实用性.     

自主移动机器人局部路径规划综述

自主移动机器人技术是近年来的研究热点,而路径规划技术是自主移动机器人技术研究中的一个重要内容。讨论了自主移动机器人路径规划技术的分类和研究局部路径规划的重要性;分析了局部路径规划技术的发展现状;指出了局部路径规划各种方法的优点与不足;对局部路径规划技术今后的发展方向做出了展望。