Time-optimal path generation for continuous and quasi-continuous path control of industrial robots

For a given continuous path, the problem of designing a time-optimal time-parametrization is considered. First, algorithms are presented which, under rather mild assumptions, yield the exact solution within two computational steps consisting of a forward and a backward computation. Then, the problem of quasi-continuous robot motion is investigated in detail. An algorithm of the same type results, but the computational burden is considerably reduced by making appropriate use of the special structure of the problem. By this, on-line use becomes feasible.Work supported by Oesterreichischer Fonds zur Foerderung der wissenschaftlichen Forschung.     

基于粒子群算法的移动机器人全局最优路径规划*

以栅格法和粒子群算法为基础,提出了一种新的机器人实时全局最优路径规划方法.该方法包括采用栅格法对环境进行建模和直接运用粒子群算法在环境模型中搜索全局最优路径.在计算机上进行了仿真,仿真结果证明了该方法的可行性和有效性.     

Design,control, and performance of the ‘weed’ 6 wheel robot in the UK MOD grand challenge

A new locomotion method for unmanned (autonomous) ground vehicles (UGV) is proposed based around six independently driven wheels mounted on three separate modules. Each module is attached to the overall robot via a pivot point and capable of independently controlling its orientation and velocity. This configuration allows the UGV to perform maneuvers conventional vehicles cannot perform, and in particular to control the body orientation separately from the movement direction. The locomotion method is mathematically analyzed to develop appropriate control algorithms and to demonstrate the vehicle performance criteria. A vehicle was constructed according to the proposed configuration and experimentally tested in the UK Ministry Of Defense grand challenge. The performance of the developed locomotion schemes helped the robot make it to the finale of the competition.     

改进A*算法的移动机器人最短路径规划

针对复杂室内环境下移动机器人路径规划存在实时性差的问题,通过对Dijkstra算法、传统A^*算法以及一些改进的A^*算法的分析比较,提出了对A^*算法的进一步改进的思路。首先对当前节点及其父节点的估计路径代价进行指数衰减的方式加权,使得A^*算法在离目标点较远时能够很快地向目标点靠近,在距目标点较近时能够局部细致搜索保证目标点附近障碍物较多时目标可达;然后对生成的路径进行五次多项式平滑处理,使得路径进一步缩短且便于机器人控制。仿真结果表明,改进算法较传统A^*算法时间减少93.8%,路径长度缩短17.6%、无90°转折点,使得机器人可以连续不停顿地跟踪所规划路径到达目标。在不同的场景下,对所提算法进行验证,结果表明所提算法能够适应不同的环境且有很好的实时性。     

A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

Robots that work in a proper formation show several advantages compared to a single complex robot, such as a reduced cost, robustness, efficiency and improved performance. Existing researches focused on the method of keeping the formation shape during the motion, but usually neglect collision constraints or assume a simplified model of obstacles. This paper investigates the path planning of forming a target robot formation in a clutter environment containing unknown obstacles. The contribution lies in proposing an efficient path planner for the multiple mobile robots to achieve their goals through the clutter environment and developing a dynamic priority strategy for cooperation of robots in forming the target formation. A multirobot system is set up to verify the proposed method of robot path planning. Simulations and experiments results demonstrate that the proposed method can successfully address the collision avoidance problem as well as the formation forming problem.     

基于量子行为烟花算法的移动机器人路径规划及平滑

针对移动机器人全局路径规划问题,提出一种基于量子行为烟花算法(quantum-behaved fireworks algorithm,QFWA)的路径规划方法.改进算法在基本烟花算法(fireworks algorithm, FWA)的基础上增加了基于量子行为的烟花爆炸策略.该策略使得种群在接近全局最优时具有较强的局部搜索能力,同时在种群远离全局最优位置时具有较强的全局搜索能力.改进算法提高了烟花爆炸产生火花的多样性和算法的收敛速度.在Benchmark测试函数上将改进算法与其他几种优化算法进行了对比,结果表明改进算法的性能优于其他算法.将QFWA应用于求解移动机器人路径规划问题,并采用均值滤波结合人工势场法对规划出的路径进行路径平滑处理.仿真实验结果表明改进方法在移动机器人路径规划问题上的可行性和有效性.     

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.     

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.     

Reduction of free-space-loss for good and rapid 3D path planning of 6DOF robots

This paper deals with a new approach to solve the up to 6DOF robots global collision-free path planning. This problem seems to be more difficult when big or very long pieces are manipulated in cluttered and occupied environments. Moreover, the computational effort increases if the necessary path resolution is very high. The developed algorithm is based on the c-space technique. Different robot models are used for rapid c-spaces computation. Each one for different parts of a typical pick and place task. The algorithm selectively uses these global or local c-spaces. This strategy leads to fast global c-space computation without a considerable loss of free-space caused by the simplified robot model, and to quasi real-time local c-space computation. The paths searching in the computed c-spaces can be performed by several techniques: cell (cube) mapping, octree, and slice, which are rule-base selected in an adequate way. Finally, the results of the algorithm implementation in several real robots are presented.     

一种未知环境下的快速路径规划方法*

为提高机器人在未知环境中的快速路径规划能力,引入自由路径表征可以通过的自由空间,引入风险函数评价机器人切入自由路径过程中发生碰撞的风险。通过搜索最优自由路径、评价碰撞风险压缩表示环境信息,使得未知环境中利用模糊控制器进行局部路径规划的实时性大为提高。与虚拟势场法等传统方法相比,其无局部最小,且极大缓解了狭窄环境中的振荡现象。实验及仿真均表明该方法实时性好、规划所得路径优于已有方法。     

一种基于改进Theta *的机器人路径规划算法

对Theta *算法进行改进,并用于解决机器人路径规划问题.首先,将障碍物对机器人产生的斥力作为一种惩罚函数加入到启发函数中,并合理地选择惩罚函数权重以确定启发函数.在此基础上,改进A *算法的变种——Theta *算法,提出对路径进行平滑处理的PS_Theta *算法.最后在二维仿真环境中进行验证及数据统计,并推广至三维复杂环境中,实验结果证明了算法的合理性与有效性     

Applying a path planner based on RRT to cooperative multirobot box-pushing

Considering robot systems in the real world, a multirobot system where multiple robots work simultaneously without colliding with each other is more practical than a single-robot system where only one robot works. Therefore, solving the path-planning problem in a multirobot system is very important. In this study, we developed a path-planner based on the rapidly exploring random tree (RRT), which is a data structure and algorithm designed for efficiently searching for multirobot box-pushing, and made experiments in real environments. A path planner must construct a plan which avoids the robot colliding with obstacles or with other robots. Moreover, in some cases, a robot must collaborate with other robots to transport the box without colliding with any obstacles. Our proposed path planner constructs a box-transportation plan and the path plans of each robot bearing the above considerations in mind. Experimental results showed that our proposed planner can construct a multirobot box-pushing plan without colliding with obstacles, and that the robots can execute tasks according to the plan in real environments. We also checked that multiple robots can perform problem tasks when only one robot could not transport the box to the goal. This work was presented in part at the 13th International Symposium on Articifial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains

A novel path-planning algorithm is proposed for a tracked mobile robot to traverse uneven terrains, which can efficiently search for stability sub-optimal paths. This algorithm consists of combining two RRT-like algorithms (the Transition-based RRT (T-RRT) and the Dynamic-Domain RRT (DD-RRT) algorithms) bidirectionally and of representing the robot–terrain interaction with the robot’s quasi-static tip-over stability measure (assuming that the robot traverses uneven terrains at low speed for safety). The robot’s stability is computed by first estimating the robot’s pose, which in turn is interpreted as a contact problem, formulated as a linear complementarity problem (LCP), and solved using the Lemke’s method (which guarantees a fast convergence). The present work compares the performance of the proposed algorithm to other RRT-like algorithms (in terms of planning time, rate of success in finding solutions and the associated cost values) over various uneven terrains and shows that the proposed algorithm can be advantageous over its counterparts in various aspects of the planning performance.     

B样条曲线融合蚁群算法的机器人路径规划

针对蚁群算法在静态环境下全局路径规划存在无法找到最短路径、收敛速度慢、路径搜索盲目性大、拐点多等问题,提出一种改进蚁群算法。以栅格地图为机器人运行环境,对初始信息素进行非均匀分布,使路径搜索更倾向于起点和目标点的连线附近;把当前节点、下一节点和目标点的信息加入启发式函数,同时引入动态调节因子,促使启发函数在迭代前期起主导作用,而后期则加强信息素引导;引入伪随机转移策略,以减少路径选择的盲目性,加快找到最短路径;动态调整挥发系数,使得前期挥发系数大,后期较小,从而避免算法陷入早熟;在最优解的基础上,引入B样条曲线平滑策略,以进一步优化最优解,使得到的路径更短且更加平滑。对改进算法的主要参数进行敏感性分析,并对该算法的各改进环节的可行性与有效性进行了实验,而且在20×20和50×50环境下与传统蚁群算法及其他改进蚁群算法进行仿真对比,实验结果验证了改进算法的可行性、有效性和优越性。     

基于Dubins路径的无人艇运动规划算法

针对无人艇运动规划问题,通过Dubins路径的理论分析,提出一种利用纯粹几何方法的Dubins路径计算方法。该方法中没有出现解方程组的运算,而是首先根据无人艇运动状态计算转向圆,然后利用几何方法计算转向圆间的公切线,最后通过公切线连接得到Dubins路径。通过5组仿真实验验证了所提方法的有效性。前4组仿真实验分别设计了计算Dubins路径过程中可能出现的各种情形,以验证算法适用于多种情况的Dubins路径计算。最后一组仿真实验用于无人艇的路径规划及运动状态调整,仿真结果表明,基于Dubins路径的无人艇运动规划算法是可行的。     

Optimum motion planning in joint space for robots using genetic algorithms

The optimum motion planning in joint space (OMPJS) for robots, which generally consists of two subproblems, optimum path planning and optimum trajectory planning, was considered as a whole in the paper. A new method for optimum motion planning problem based on an improved genetic algorithm is proposed, which is more general, flexible and effective. This approach incorporates kinematics constraints, dynamics constraints, and control constraints of robotic manipulator. The simulation results for a two and a three degrees of freedom robots are presented and discussed. The simulations are based on genetic algorithm class library WGAClass 1.0 developed by us with Borland C++ 3.1.     

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。     

基于自组织LMBPNN的移动机器人路径规划器

提出一种自组织LMBP神经网络,并将之用于移动机器人免碰路径规划。该算法首先用基于距离传感器的底层局部路径规划器生成初始路径,然后用自组织神经网络将该路径进行样本数据分类,之后将自组织神经网络的权值作为LMBP的输出样本,移动机器人的起始点与目标点作为LMBP神经网络的输入样本进行学习。这样,不但解决了三层LMBP样本若庞大则增加存贮、运行成本,以及数据冗余问题,并且随着机器人对未知环境探索的增多,所构建的地图越趋丰满。仿真结果说明该方法很好效。     

基于情感与环境认知的移动机器人自主导航控制

将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力     

基于新人工势场函数的机器人动态避障规划

人工势场法是进行机器人路径规划时常用的方法,但若用圆锥曲线函数作为引力场数学模型时,在目标点会产生抖动问题.本文在分析抖动产生原因的基础上,增加一个指数项到引力场函数中,从而消除了奇异值点,避免了抖动现象.然后将一敏感度参数引入斥力场函数,以便灵活控制运动过程中机器人与障碍物距离的大小.通过对敏感度的调节,还可以克服传统势场法中目标点在斥力作用范围内时,机器人无法到达目标点的缺陷.最后给出新势场法的仿真.