Previous work has shown that it is possible to guarantee a reachable workspace for a kinematically redundant robot after an arbitrary locked-joint failure if one artificially restricts the range of its joints prior to the failure. Identifying the optimal articial joint limits has been the subject of previous work to maximize this so-called “failure-tolerant workspace.” Unfortunately, these techniques are not feasible for a highly redundant robot operating in a spatial workspace. This work presents a novel hybrid technique for estimating the failure-tolerant workspace size for robots of arbitrary kinematic structure and any number of degrees of freedom performing tasks in a 6D workspace. The method presented combines an algorithm for computing self-motion manifold ranges to estimate workspace envelopes and Monte-Carlo integration to estimate orientation volumes to create a computationally efficient algorithm. This algorithm is then combined with the coordinate ascent optimization technique to determine optimal artificial joint limits that maximize the size of the failure-tolerant workspace of a given robot. This approach is illustrated on multiple examples of robots that perform tasks in 3D planar and 6D spatial workspaces.

中文翻译：

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一种估计运动冗余机器人容错工作空间大小的混合方法

先前的工作表明，如果在故障前人为地限制其关节的范围，则可以在任意锁定关节故障后保证运动学冗余机器人的可达工作空间。确定最佳人工关节限制一直是以前工作的主题，以最大化这个所谓的“容错工作空间”。不幸的是，这些技术对于在空间工作空间中运行的高度冗余的机器人是不可行的。这项工作提出了一种新颖的混合技术，用于估计具有任意运动结构和任意数量自由度的机器人在 6D 工作空间中执行任务的容错工作空间大小。提出的方法结合了计算自运动流形范围的算法来估计工作空间包络和蒙特卡洛积分来估计方向体积，以创建计算效率高的算法。然后将该算法与坐标上升优化技术相结合，以确定使给定机器人容错工作空间最大化的最佳人工关节限制。在 3D 平面和 6D 空间工作空间中执行任务的多个机器人示例中说明了这种方法。