ReachBot, a proposed robotic platform, employs extendable booms as limbs for mobility in challenging environments, such as martian caves. When attached to the environment, ReachBot acts as a parallel robot, with reconfiguration driven by the ability to detach and re-place the booms. This ability enables manipulation-focused scientific objectives: for instance, through operating tools, or handling and transporting samples. To achieve these capabilities, we develop a two-part solution, optimizing for robustness against task uncertainty and stochastic failure modes. First, we present a mixed-integer stance planner to determine the positioning of ReachBot's booms to maximize the task wrench space about the nominal point(s). Second, we present a convex tension planner to determine boom tensions for the desired task wrenches, accounting for the probabilistic nature of microspine grasping. We demonstrate improvements in key robustness metrics from the field of dexterous manipulation, and show a large increase in the volume of the manipulation workspace. Finally, we employ Monte-Carlo simulation to validate the robustness of these methods, demonstrating good performance across a range of randomized tasks and environments, and generalization to cable-driven morphologies. We make our code available at our project webpage, https://stanfordasl.github.io/reachbot_manipulation/

翻译：ReachBot是一种新型机器人平台，采用可伸缩悬臂作为肢体，可在火星洞穴等复杂环境中移动。当与环境连接时，ReachBot表现为并联机器人，其可重构性源于悬臂的分离与重新部署能力。这种能力使其能够实现以操控为核心的科研目标：例如操作工具、处理及运输样本。为实现这些功能，我们提出一种两阶段解决方案，以优化对任务不确定性和随机失效模式的鲁棒性。首先，我们提出一种混合整数站位规划器，用于确定ReachBot悬臂的布局，以最大化关于名义点（nominal point）的任务力旋量空间。其次，我们提出一种凸张力规划器，在考虑微棘爪抓取概率特性的基础上，确定实现期望任务力旋量所需的悬臂张力。我们展示了关键鲁棒性指标（源于灵巧操控领域）的改进，并揭示了操控工作空间体积的大幅增长。最后，采用蒙特卡洛仿真验证了这些方法的鲁棒性，证明其在多种随机化任务与环境中均表现优异，并可泛化至索驱动机器人形态。相关代码已开源至项目主页：https://stanfordasl.github.io/reachbot_manipulation/