基于似人特性的机器人拟人臂自主抓取动作规划

针对拟型服务机器人在家庭环境中的自主抓取任务,提出了一种强调运动姿态似人特性的机器人手臂动作的运动规划方法.该方法基于人体工程学中的快速上肢评估准则评价机器人运动姿态的似人特性,并在此基础上以机器人传速速率最优为目标规划机器人持物动作的姿态构型.最后以Motoman SDA10D拟人双臂机器人为例,具体介绍了该方法的应用和规划的结果,规划结果证明了该方法的可行性和有效性.     

关节型机器人的似人操作构型规划

对关节型机器人的操作构型进行规划时,本文提出了一种新的规划方法,它结合了似人评估准则和机器人传速特性优化的概念,可实现有最大末端传速特性的操作,同时与人类手臂操作的特点最为相似.该方法首先利用应用人体工程学中的快速上肢评价准则(RULA)对机器人的操作空间进行划分,然后在各子空间内最大化机器人末端沿指定方向的传速速率.最终选定一个最符合人类操作特性又同时满足操作任务的机器人操作构型.通过在2自由度平面机器人和7自由度拟人机械臂上的规划实验进一步展示了本方法的使用,规划结果验证了其有效性.     

基于分级规划策略的拟人机械臂仿人运动规划算法研究

为使拟人机械臂具有高精度的仿人运动,提出一种通过触发条件和分级规划策略 的仿人运动新方法。将人臂运动过程离散为不同运动阶段,在每一个运动阶段都有与之对应的 规划层,在不同的规划层中,拟人机械臂的运动特点不同。利用各自的特点建立不同规划层下 的运动模型及臂姿预测指标,对拟人机械臂臂姿进行预测。最后,以NAO 机器人为实验平台, 比较所提方法与最小势能法(MTPE)的静态臂姿与动态臂姿预测,并与运动捕捉系统(OptiTrack) 采集的真实人臂运动数据进行比较。实验表明,该方法具有较小的静态臂姿和动态臂姿预测误 差,能使拟人机械臂产生高度逼真的仿人运动。     

空间机器人捕获运动目标的协调规划与控制方法

针对目标以任意轨迹运动且其轨迹可能与``有保证工作空间'不相交的问题, 提出了空间机器人捕获运动目标的协调规划与控制方法. 首先, 根据手眼视觉测量数据, 预测目标的运动路径, 由此确定空间机器人对目标的最优交会姿态及最佳捕获臂型; 其次, 规划基座姿态及机械臂关节角的轨迹; 最后, 采用协调控制的方法, 实现空间机器人系统对运动目标的最优捕获(以最优交会姿态及最佳捕获臂型对目标进行捕获). 仿真结果表明了该方法的有效性.     

基于空间几何方法的乒乓球机器人最优姿态求取

首先分析了仿人机器人手臂的空间几何构型,特别是针对在给定任务下末端执行器的姿态约束不完整的情况.并在此基础上,以机器人手臂的整体动能最小为目标,优化球拍绕其法线方向的旋转角.同时从整个机器人手臂动能的方面对机器人手臂的末端执行器姿态进行优化分析,并确定最优条件,即手臂所构成的平面与球拍运动速度向量所形成的平面互相垂直,然后根据最优条件及各连杆间的关系建立方程,求解出最优的末端姿态角,以减少机器人手臂运动所引起的振动.最后,通过仿真结果从动能求取和轨迹规划等方面验证了提出的方法的有效性.     

机器人运动规划中的姿态空间建模方法研究

提出机器人运动规划中的一种姿态空间建模方法,其基本思想是在工作空间中定义具有某种特征的点为基本碰撞体,可用于机械臂在复杂环境中的运动规划建模。实验表明该建模方法是有效可行的。     

基于PSO的空间机械臂轨迹规划技术研究

研究机器人的机械臂轨迹优化问题,空间机器人系统的机械臂和基座之间存在动力学耦合,基座姿态稳定性容易受到机械臂运动的反作用干扰.为了使空间机器人系统的姿态稳定性不受的影响,提出了一种基于PSO(粒子群优化算法)的参数化5-3-5轨迹规划方法.利用PSO的优化能力找到合适的参数组合,进行关节空间轨迹规划.通过对比实验,运用动力学仿真对方法的有效性进行了验证.仿真结果表明,方法规划出的轨迹运动光滑,且按段轨迹运动机械臂对基座姿态的扰动符合预期要求,证明规划轨迹的有效性.     

基于ADAMS的双足机器人拟人行走动态仿真

在双足机器人HEUBR_1的设计中,下肢采用了一种新的串并混联的仿人结构,并在足部增加了足趾关节.为验证该仿人结构设计的合理性及拟人步态规划的可行性,在ADAMS虚拟环境中建立了双足机器人HEUSR_1的仿真模型.通过拟人步态规划生成了运动仿真数据,在ADAMS虚拟环境中实现了具有足趾运动的拟人稳定行走,经仿真分析,获得了双足机器人HEUBR_1拟人行走步态下的运动学和动力学特性,仿真结果表明:双足机器人HEUBR_1的串并混联的仿人结构设计能够满足行走要求,且拟人步态规划方法可行,有足趾运动的拟人行走具有运动平稳、能耗低、足底冲击力小的特点.稳定行走的仿真步态数据可为下一步双足机器人HEUBR_1样机行走实验提供参考数据.     

提高柔性冗余度机器人动态特性的最小变形能法

冗余度柔性机器人的运动规划是机器人领域的重要前沿课题之一 .利用此机器人的冗余特性 ,可以改善其运动学和动力学性能 .柔性机器人的变形能能够很好地反映出其整体弹性变形程度 .本文提出了在最小变形能意义下的柔性冗余度机器人运动学规划的新方法 .以平面三柔性臂机器人为例进行了仿真 ,通过与最小末端误差意义下的规划策略进行比较 ,充分显示了最小变形能法在提高柔性机器人动态性能的有效性和优越性     

冗余自由度协作机器人运动规划研究

针对传统快速探索随机树(Rapidly Exploring Random Tree,RRT)路径规划算法在新枝点扩展上缺乏目标性和时间复杂度高的问题,以具有偏置构型的7自由度冗余机器人Rethink Sawyer为研究对象,利用引力势函数,对新枝点的扩展进行调节,提出了具有导向性的变步长双向RRT扩展算法完成协作机器人运动规划.采用阻尼最小二乘Newton-Raphson方法对Sawyer机器人逆运动学进行数值求解.通过机械臂运动学模型和运动规划算法仿真、ROS平台的运动规划实验,验证了改进RRT算法进行路径规划的正确性,在操作过程中有效地避免了与约束框架碰撞,降低了机械臂运动规划的时间复杂度.     

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.     

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.     

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.     

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.     

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。     

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.     

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。