用接触降低不确定性，让机器人精准完成缝隙装配
Bi-Level Belief Space Search for Compliant Part Mating Under Uncertainty
- 分两层搜索：先定接触顺序，再找柔性运动路径
- 在模拟和真实机器人上均成功完成低间隙插入任务
- 适合需要高精度装配的工业场景
低间隙部件的自主装配对机器人仍具挑战。本文提出基于模型的双层信念装配（BILBA）规划器，通过利用与环境的接触来减少不确定性，计算出一系列柔性运动序列以完成困难装配任务。方法首先从部件构型空间障碍物结构推导候选接触时序，再寻找实现期望接触的柔性运动。实验表明，BILBA可在多个模拟任务及真实机器人矩形插销入孔任务中高效生成鲁棒规划。
原文摘要 · Abstract (English)
The problem of mating two parts with low clearance remains difficult for autonomous robots. We present bi-level belief assembly (BILBA), a model-based planner that computes a sequence of compliant motions which can leverage contact with the environment to reduce uncertainty and perform challenging assembly tasks with low clearance. Our approach is based on first deriving candidate contact schedules from the structure of the configuration space obstacle of the parts and then finding compliant motions that achieve the desired contacts. We demonstrate that BILBA can efficiently compute robust plans on multiple simulated tasks as well as a real robot rectangular peg-in-hole insertion task.
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