Robot performance measures are important tools for quantifying the ability to carry out manipulation tasks. Generally, these measures examine the system’s kinematic transformations from configuration to task space. This means that environmental constraints are neglected in spite of the significant effects they may have on the robot’s admissible motions. In this paper, we propose a new measure called the constrained manipulability polytope (CMP) that considers the system’s kinematic structure, including closed chains or composite sub-mechanisms, joint limits and the presence of obstacles. For an illustrative planar case, we demonstrate how the CMP can evaluate a robot’s performance in a cluttered scene and how this evaluation can be extrapolated to obtain a workspace visualization. Additionally, we show the advantages and limitations of the CMP compared to the state of the art. Furthermore, the method is demonstrated both in simulation and experimentally for NASA’s Valkyrie robot. We show how the CMP provides a measure for single-arm and dual-arm manipulation tasks, analyze the workspace and be used to optimize the robot’s posture.

中文翻译：

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使用速度多面体的人形机器人的约束可操作性

机器人性能测量是量化执行操作任务能力的重要工具。通常，这些测量检查系统从配置到任务空间的运动学转换。这意味着尽管环境约束可能对机器人的可接受运动产生重大影响，但它们仍被忽略。在本文中，我们提出了一种称为约束可操作性多面体 (CMP) 的新度量，它考虑了系统的运动结构，包括闭链或复合子机制、关节限制和障碍物的存在。对于一个说明性的平面案例，我们演示了 CMP 如何评估机器人在杂乱场景中的性能，以及如何推断该评估以获得工作空间可视化。此外，我们展示了 CMP 与现有技术相比的优势和局限性。此外，该方法在 NASA 的 Valkyrie 机器人的模拟和实验中都得到了证明。我们展示了 CMP 如何为单臂和双臂操作任务提供测量，分析工作空间并用于优化机器人的姿势。