基于似人特性的拟人臂机器人自主抓取动作规划

针对拟人臂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拟人臂动作的规划方法.该方法分为两个部分,构型规划和运动规划,构型规划基于人体工程学中广泛使用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末端传速速率最优为目标规划机器人运动过程中的关键构型.运动规划结合了Fitts定律和似人运动模型规划了机器人末端的空间轨迹.本文最后以Motoman SDA10D拟人双臂机器人和Mitsubishi PA10机器人为例,具体介绍了该方法的应用和规划的结果,规划结果证明了本方法的可行性和有效性.     

“主动适应”：从教育规律视角的认识

当现代大学越来越成为国家实力和国家综合竞争力的象征和标志时,“主动适应”,即大学人才培养主动适应经济社会发展的需要,或大学主动为经济社会发展服务,似乎更多关注现代大学承载的社会责任,而相应较少关注高等教育的规律。然而,“主动适应”是高等教育的办学理念,因而应从高等教育规律上进行认识和把握。     

A heuristic approach to automatic grasp planning for a 3-fingered hand

This paper presents the core of a software system able to determine a good grasp configuration on 3D objects for a three-fingered hand. The grasp planning problem has been studied considering both the constraints due to the stability and accessibility conditions, and the ones related to functionality. Physical, geometrical, spatial and task-related knowledge for solving the grasp planning problem have been properly modelled to support a heuristic-based reasoning process. A series of heuristic rules and geometric tests are used to scan the solution space, searching for a good grasp. In fact, when considering the three-dimensional case, a purely analytical and exhaustive approach appears too complex because of the dimension of the search space. This approach results in an incremental and modular model of grasp reasoning, that has been implemented using the Flex expert system shell. This work has been developed and demonstrated within the Esprit 2 project CIM-PLATO No. 2202.     

基于深度神经网络的多指灵巧手抓取手势优化

基于深度神经网络模型,提出了一种适用于多指灵巧手的抓取手势优化方法。首先,在仿真环境下构建了一个抓取数据集,并在此基础上训练了一个卷积神经网络,依据目标物体单目视觉信息和多指灵巧手抓取位形来预测抓取质量函数,由此可以将多指灵巧手的抓取规划问题转化为使抓取质量最大化的优化问题,进一步,基于深度神经网络中的反向传播和梯度上升算法实现多指灵巧手抓取手势的迭代与优化。在仿真环境中,比较该网络和仿真平台对同一抓取位形的抓取质量评估结果,再利用所提出的优化方法对随机搜索到的初始手势进行优化,比较优化前后手势的力封闭指标。最后,在实际机器人平台上验证本文方法的优化效果,结果表明,本文方法对未知物体的抓取成功率在80%以上,对于失败的抓取,优化后成功的比例达到90%。     

调整国有经济战略布局是国有企业改革的重大举措

经济布局通常是指一国经济资源在地城空间和部门空间上的配置及运行状况。长期以来,我国国有经济的布局由于受计划经济体制的制约,存在诸多不合理因素,一是行业部门分布太广,二是小企业占绝大多数。十五大报告指出：要对国有经济布局进行战略调整。本文分析了调整国有经济战略布局的背景和目标,论述了调整国有经济布局的基本原则,指出了调整国有经济布局需要注意的几个问题。     

多自由度仿生假手嵌入式控制系统及其抓取策略

为一种能够实现5 指独立动作以及具备人机交互能力的多自由度仿生假手设计了手部嵌入式控制系 统．该系统由传感器系统和运动控制系统构成,集成于假手机体内部,通过通信总线与上层控制器交换信息．传感 器系统包括3 种类型,共12 个传感器,可为假手自主抓取以及人机交互中的感觉反馈提供数据,运动控制系统用于 控制、驱动各手指动作．此外,本文以基于位置的阻抗控制为底层,以动作预构形为上层设计了分层控制策略．实 验表明,该嵌入式控制系统和分层控制策略使假手实现了自主抓取功能,提高了抓取的柔顺性、稳定性和适应性．     

多指机器人手协调控制研究进展

本文综述了关于多指机器人手协调控制方面的研究进展情况．文章分为三个部分．第 一部分介绍了多指机器人手各方面的研究现状;第二部分讨论协调控制,主要是力分配算法 方面的研究成果;第三部分探讨了自主抓取的实现方法,并提出对未来研究的设想．     

A Theoretical Approach of an Intelligent Robot Gripper to Grasp Polygon Shaped Objects

This paper presents an approach for grasp planning and grasp forces optimization of polygon shaped objects. The proposed approach is an intelligent rule-based method that figures out the minimal number of fingers and minimal values of contact forces. These fingers are required to securely grasp a rigid body in the presence of friction and under the action of some external force. This is accomplished by finding optimal contact points on the object boundary along with minimal number of fingers required for achieving the aforementioned goal. Our system handles every object case independently. It generates a rule base for each object based on adequate values of external forces. The system uses the genetic algorithm as its search mechanism, and a rule evaluation mechanism called bucket brigade for the reinforcement learning of the rules. The process mainly consists of two stages; learning then retrieval. Retrievals act on line utilizing previous knowledge and experience embedded in a rule base. If retrievals fail in some cases, learning is presumed until that case is resolved. The algorithm is very general and can be adapted for interface with any object shape. The resulting rule base varies in size according to the degree of difficulty and dimensionality of the grasping problem.     

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.     

融合注意力机制和多任务学习的机器人抓取检测算法

抓取主要分为抓取检测、轨迹规划和执行环节，准确的抓取检测是完成抓取任务的关键。为进行更准确的抓取检测，提高机器人抓取性能表现，本研究以关键点检测算法为基础，提出了一种融合注意力和多任务学习的抓取检测算法。首先，针对任务特点，在特征提取环节引入CA(coordinate attention)注意力模块，显式的学习通道和空间特征，充分利用特征信息。其次，在损失函数环节加入多任务权重学习算法，学习抓取中心坐标、抓手开合宽度及旋转角度信息的最优权重。最后，在Cornell数据集以及更大规模的Jacquard数据集上进行试验。研究结果表明，所提方法相比滑动窗口和锚框类型等经典方法在检测速率上有明显提升，且与单纯的关键点检测方法相比有更高的准确率，所提模型在两个数据集上分别取得98.8%和95.7%的准确率。检测示例体现出所提模型对于非常规物体也有良好的抓取结果，不同Jaccard系数条件下的抓取结果显示模型在精准抓取方面有优秀性能，而对于权重学习算法的不同初始值试验则表明所提模型具有良好的鲁棒性。此外，通过消融实验分析了不同模块对于模型性能表现的影响程度。