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首页> 外文会议>American Society of Mechanical Engineers(ASME) Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2004(DETC 2004) vol.2 pt B: Biennial Mechanisms and Robotics Conference; 20040928-1002; Salt Lake City,UT(US) >MANIPULATOR TASK-BASED PERFORMANCE OPTIMIZATION
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MANIPULATOR TASK-BASED PERFORMANCE OPTIMIZATION

机译：机械手基于任务的性能优化

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摘要

This research uses new developments in redundancy resolution and real-time capability analysis to improve the ability of an articulated arm to satisfy task constraints. Task constraints are specified using numerical values of position, velocity, force, and accuracy. Inherent in the definition of task constraints is the number of output constraints that the system needs to satisfy. The relationship of this with the input space (degrees of freedom) defines the ability to optimize manipulator performance. This is done through a Task-Based Redundancy Resolution (TBRR) scheme that uses the extra resources to find a solution that avoids system constraints (joint limits, singularities, etc.) and satisfies task constraints. To avoid system constraints, we use well-understood criteria associated with the constraints. For task requirements, the robot capabilities are estimated based on kinematic and dynamic manipulability analyses. We then compare the robot capabilities with the user-specified requirement values. This eliminates a confusing chore of selecting a proper set of performance criteria for a task at hand. The breakthrough of this approach lies in the fact that it continuously evaluates the relationship between task constraints and system resources, and when possible, improves system performance. This makes it equally applicable to redundant and non-redundant systems. The scheme is implemented using an object-oriented operational software framework and its is demonstrated in computer simulations of a 10-DOF manipulator.
机译：这项研究使用了冗余解决方案和实时能力分析方面的新发展，以提高关节臂满足任务约束的能力。使用位置，速度，力和精度的数值指定任务约束。任务约束的定义中固有的是系统需要满足的输出约束的数量。它与输入空间（自由度）的关系定义了优化机械手性能的能力。这是通过基于任务的冗余解决方案（TBRR）方案完成的，该方案使用额外的资源来找到避免系统约束（联合限制，奇异性等）并满足任务约束的解决方案。为了避免系统约束，我们使用与约束相关的易于理解的标准。对于任务要求，基于运动学和动态可操纵性分析来估计机器人的能力。然后，我们将机器人功能与用户指定的需求值进行比较。这消除了为手头的任务选择适当的性能标准集的麻烦工作。这种方法的突破在于，它可以连续评估任务约束和系统资源之间的关系，并在可能的情况下提高系统性能。这使得它同样适用于冗余和非冗余系统。该方案是使用面向对象的操作软件框架实现的，并在10自由度机械手的计算机仿真中得到了证明。

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