一种有腿机器人步态轨迹生成算法

目前存在一类有腿机器人,由于其底层动力学控制参数难于获取,行走设计无法使用已有的控制方法,现有的基于建模的步态存在缺乏完善规划,固定规划步态与实际步态相差较大的问题,针对这个问题,提出一种使用曲线拟合生成步态轨迹的想法,通过引入遗传算法,让机器人能自主的搜索良好的行走步态轨迹,在四足步行机器人平台上取得了良好的实验结果.     

下肢外骨骼机器人变步长步态规划方法

为了解决下肢外骨骼机器人连续步态规划问题,基于倒立摆模型提出了一种步态规划算法,并针对传统倒立摆模型无法变步长连续行走的问题提出了新的改进方法。将外骨骼机器人分成支撑腿和摆动腿两部分,分别采用D-H法进行运动学分析;利用倒立摆模型和固定函数法,进行等效质心与摆动腿末端轨迹规划;在相邻单脚支撑期之间插入双脚支撑期,使下肢外骨骼机器人在不断改变步行时,利用双脚支撑期进行位置和速度的切换,实现实时步态规划;将规划算法在SIMULINK中实现,并与ADAMS模型进行联合仿真,下肢外骨骼机器人在仿真环境下行走稳定,证明了算法的有效性。     

双足爬壁机器人壁面凹过渡步态规划研究

针对腿足式爬壁机器人在壁面过渡时的步态规划问题，以一种真空吸附式双足爬壁机器人为研究对象，在步态分析的基础上，基于有限状态机建立了机器人的步态模型，进而提出了基于加权插值和BP神经网络的双足爬壁机器人壁面凹过渡在线步态规划算法，为提高机器人壁面过渡的自主控制能力奠定了基础．仿真分析和实验结果表明，该步态规划算法对于实际的机器人系统是有效的和可行的。     

基于虚拟样机技术的四足机器人静态稳定步态规划

针对现有技术文献中广泛使用的多种静态稳定步态中速度稳定性与稳定裕度不可兼得的通病,在随动质心的静态步态基础上,利用参数化坐标变换矩阵方法规划出一种四足机器人前进过程中质心以曲线轨迹移动的静态步态方法,使该步态方法以连续性速度运动的过程中保证一定稳定裕度;通过D-H法求得四足机器人的逆运动学坐标变换矩阵,分别在三维空间中对四足机器人的四组足端轨迹方程进行规划,并带入MATLAB软件后以逆运动学方程计算出关节夹角驱动方程,利用步态规划图求出机器人四条腿各自对应的夹角驱动方程以及机体质心轨迹方程;最后在MSC.ADAMS软件中建立四足机器人虚拟样机并对规划的步态进行虚拟仿真,仿真结果验证了该步态对提升四足机器人对于速度连续性以及稳定裕度的提升。     

四足机器人步态分析及仿真实现

为实现一种液压驱动的四足机器人稳定行走,根据机器人稳定裕量原则,针对JQRI00四足机器人进行了步态规划。根据前期对四足机器人的结构设计,建立了JQRI00四足机器人的虚拟样机;针对其结构特点,分别设计了四足机器人在进行直线行走和转弯时的步态规划,应用ADAMS对虚拟样机进行了直线步态仿真。仿真结果准确、合理,表明所研究的步态规划正确、可行,能够实现四足机器人稳定行走,为机器人后期进行驱动控制奠定了基础。     

一种六足仿生巡检机器人的设计与实现

六足仿生机器人因其灵活度好、可靠性高、适应性强等特点而得到广泛应用;针对六足仿生巡检机器人,从结构设计、步态规划、系统仿真和实物构建等方面,探索一般意义上系统设计和实现方法;首先设计了六足仿生机器人的多关节机械结构,并给出了此类系统的量化建模方法;然后采用了重心随动的三角步态规划方法,对系统稳定性和典型步态规划进行了量化分析;在此基础上基于标准D-H参数法建立了机器人的运动学模型,并且通过仿真实现了六足机器人向前纵向行走和向右横向行走的直线平稳运动;最后通过六足仿生巡检机器人实物测试,验证了所设计的结构和步态规划方法的可行性和有效性。     

基于遗传算法的四足机器人行走步态规划

因RoboCup四腿组比赛所采用机器人的关节刚度较低，采用固定形状行走轨迹的步态规划方法所产生的实际步态和规划步态偏差较大。这种偏差限制了机器人行走速度的提高。为了提高机器人的行走速度，在原有步态规划方法的基础上，引入200组坐标描述任意形状的机器人行走轨迹，并用改进的遗传算法寻找最适合机器人行走的轨迹形状。实验结果表明：改进的行走轨迹规划方法经学习后的实际步态更有利于机器人的行走，在行走更加平稳的同时可使机器人获得更快速的行走效果。     

基于改进蚁群算法的四足机器人步态规划

四足机器人关节众多、运动方式复杂,步态规划是四足机器人运动控制的基础。传统的算法多基于仿生原理,缺乏广泛适应性。 在建立运动学方程的基础上,提出了一种基于改进蚁群算法的步态规划算法。该算法利用了四足机器人4条腿运动的线性无关性,将步态规划问题转换为在四维空间里求取最长路径问题。仿真结果表明,该算法得出了满足约束条件的所有步态,最后通过机器人样机检验,验证了该算法求取结果的有效性和合理性。     

仿人机器人斜坡行走的步态规划

为了解决双足机器人在复杂路面的行走问题,提出了一种具体的基于几何约束的机器人斜坡行走步态规划方法。通过对机器人行走的起步阶段,单脚支撑期中摆动腿对机器人身体稳定的影响,以及行走步态流程进行了详细的规划,以机器人Nao为研究对象,构建出了机器人的连杆模型,计算出机器人在前向和侧向运动中保证身体稳定的踝关节约束范围,分析了流程图中机器人各行走步态,并计算出各步态中关节角度变化,从而规划出了机器人Nao从起步到结束行走的过程。运用三次多项式插值的方法使得各关节运动平滑稳定,并根据规划中的各个步态运动利用MATIAB仿真,获得机器人在步行过程中X、Y、Z方向上的质心轨迹,并通过x、y方向的轨迹可以看出机器人行走过程重心稳定,从而证明此方法用于机器人斜坡行走的可行性。     

六边形对称分布六腿机器人的典型步态及其运动性能分析

为了便于在不同地理条件下合理地选择较优的步态,实现稳定高效的智能行走,本文针对一种六边形 对称分布的六腿机器人研究其不同步态的优劣．主要从行走能力、稳定性和能耗3 个角度对六边形对称结构的六腿 机器人在同样占空比下的3 种静态稳定周期步态进行了比较研究,此外还简要分析了其越障能力和穿越窄道的能 力．研究分析结果表明3 种步态（横向昆虫式摆动步态、哺乳动物式踢腿步态和混合步态）在不同条件下各有优劣： 横向昆虫式摆动步态在能耗和越障能力方面较其他两种步态有优势;而混合步态在稳定性上最具优势,其它能力处 于中间;哺乳动物式踢腿步态则可穿越窄道,步长上较昆虫摆动步态略好．本文的研究工作为六边形对称结构的六 腿机器人在未知复杂地貌环境下的智能行走提供了重要参考．     

CBPOP: A Domain-Independent Multi-Case Reuse Planner

The reuse of multiple cases to solve a single planning problem presents a promise of better utilization of past experience over single-reuse planning, which can lead to better planning performance. In this paper, we present the theory and implementation of CBPOP, and show how it addresses the multi-reuse planning problems. In particular, we present novel approaches to retrieval and refitting. We also explore the difficult issue of when to retrieve in multi-reuse scenarios, and we empirically compare the results of several solutions we propose. Results from our experiments show that the best ranking function for pure generative planning is not necessarily the best ranking function for multi-reuse planning. The surprising result in the reuse scenarios is that the single-goal case library performed better than larger case libraries consisting of solutions to multi-goal problems.     

A survey on coverage path planning for robotics

Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners and inspection of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing on the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works.     

Complete and Rapid Regrasp Planning with Look-up Table

A pick-and-place operation in a 3-dimensional environment is a basic operation for humans and multi-purpose manipulators. However, there may be a difficult problem for such manipulators. Especially, if the object cannot be moved with a single grasp, regrasping, which can be a time-consuming process, should be carried out. Regrasping, given initial and final poses of the target object, is a construction of sequential transition of object poses that are compatible with two poses in the point of grasp configuration. This paper presents a novel approach for solving the regrasp problem. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given robot, generates a look-up table which has information on kinematically feasible task space of the end-effector throughout the entire workspace. Then, using this table, the planning automatically determines a possible intermediate location, pose and regrasp sequence leading from the pick-up to put-down grasp. With a redundant robot, it is shown experimentally that the presented method is complete in the entire workspace and can be implemented in real-time applications due to rapid regrasp planning time. The regrasp planner was combined with an existing path.     

A privacy-preserving model for multi-agent propositional planning

Abstract

Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents’ privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents’ privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics.     

Successful strategic information systems planning

Abstract. Strategic information systems planning (SISP) is the process of ensuring alignment between business plans and objectives and information systems plans and objectives and/or the process of identifying information systems which will provide the organization with a competitive edge. This paper distinguishes SISP from both tactical and operational information systems planning, discusses the reasons for management concern with SISP, summarizes problems which may be encountered with the planning process and resultant plant implementation and then focuses on the issue of SISP success. Specifically, success is considered not only in terms of how it may be achieved but also in terms of how it can be defined (i.e. what constitutes SISP success) and how SISP success can be measured. Specific research questions addressing the measurement of SISP success are identified.     

不确定可逆规划的强循环规划解

动作的执行在理想情况下是确定的,但现实生活中常常因为意外情况的发生而造成了不确定性,并产生不利影响.针对这种情况,建立了一种新的不确定规划模型,在不确定规划中增加了两个约束:1)所有动作的执行是可逆的;2)若一个状态在理想情况下不能达到目标,那么它不能企图在执行一个动作时发生意外而接近或达到目标.在该模型下设计了求解强循环规划的算法,首先只考虑所有动作的执行是在理想情况下发生的,这时可以将规划子图转换为规划子树并求出规划子树中每个状态的可达性;接下来考虑所有动作执行意外的情况,若动作被意外执行之后不能到达目标状态,则删除这个动作并更新规划子图和规划子树,最后通过遍历规划子图和规划子树求强循环规划解.考虑到有些意外的发生并不可预知,该算法能够在意外发生时只对部分失效的规划解进行更新而不需要重新求规划解.实验结果证明该算法能够快速地更新规划解且与问题的规模大小无关.     

Information systems planning: Lessons from strategic planning

IS planning has changed considerably since its inception as an operational planning tool, expanding its scope to address key business objectives and forging a link with strategic business planning. A great deal of attention is currently focused on identifying and developing strategic information systems. Strategic planning has also become more broadly-focused over time, as simple forecasting techniques have been replaced by a variety of adaptive approaches that emphasize industry structure analysis and environmental dynamics. However, strategic planning has come under fire recently for failing to live up to its promises. As these two disciplines become more closely aligned, planners can learn from the experiences of strategic planners and, hopefully, avoid a similar crisis of confidence.     

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.     

分层法求强循环规划解

设计了一种求解强循环规划问题的状态分层算法。从目标状态开始,首先进行强规划分层,然后对剩余状态进行弱规划分层,并记录相应信息,最后用该信息作启发因子,在弱规划分层结果中搜索强循环规划分层。分层结束后利用分层时记录的信息可以直接得到强循环规划解。所设计的算法在求解状态动作较多的强循环规划问题时有较高的效率;且当强规划解存在时,求解效率更高,并能保证得到质量更优的强循环规划解——强规划解。实验表明,所设计的算法能够以较少的重复搜索得到强循环规划解,求解效率比反向搜索高。     

过度规划、部分可满足规划及简单偏好综述*

智能规划是人工智能的重要分支,它从某个特定问题的初始状态出发,寻找达到解决该问题的目标状态的动作序列,但是在许多真实问题中,往往没有足够的资源来实现所有目标。因此,智能规划中对软目标的研究包括过度规划、部分可满足规划和简单偏好,已成为近年来的热点。将对过度规划、部分可满足规划和简单偏好的主要研究成果进行综述,包括这几类问题的差异比较、主要解决方案以及对将来的展望。