RI: Small: Taming Combinatorial Challenges in Multi-Object Manipulation

RI：小：克服多对象操纵中的组合挑战

基本信息

批准号：
1617744
负责人：
Kostas Bekris
金额：
$ 46.84万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617744&HistoricalAwards=false
关键词：
RI Small Taming Combinatorial Challenges

项目摘要

A key challenge in many autonomous robot manipulation applications isthe rearrangement of multiple objects. There are two situations wheresuch needs arise: (i) the manipulation task itself is to rearrangeobjects, and (ii) occluding items must be rearranged to allow therobot access to the target object(s). Examples of such scenarios canarise in warehouses and industrial setups, where a robot has tofrequently select, pick and transfer products, packages and pallets inthe presence of many other similar objects. Another example comes fromservice robotics, where a robotic assistant that operates in a humanspace has to frequently retrieve or rearrange multiple items placed innarrow spaces, such as objects in shelves.This project investigates which classes of multi-object manipulationplanning can be efficiently addressed given progress in multi-bodymotion planning and develops a powerful suite of novel computationalsolutions. The key insight is that for many real-world rearrangementtasks the sequence of object motions to solve the problem, ignoringgrasping aspects, look similar to solutions of multi-body motionplanning, especially for similar sized objects. The study of this linkreveals it is possible to cast certain multi-object manipulationproblems as a "pebble motion problem on a graph", which is wellstudied in algorithmic theory and multi-body motion planning. Theoverall objective is to provide rigorous methods with desirablecompleteness and optimality guarantees for multi-object manipulation,which exhibit good scalability and efficiency for problems wherecurrent methods face issues with the inherent combinatorialcomplexity. Such methods could also be used as guiding heuristics fortasks with additional constraints, such as non-trivial dynamics anduncertainty.

许多自动驾驶机器人操纵应用中的主要挑战是多个对象的重新安排。出现了两种情况：（i）操纵任务本身是重新安排对象，并且（ii）必须重新安排遮挡项目以允许允许对目标对象的访问。此类场景的示例在仓库和工业设置中加油，在存在许多其他类似物体的情况下，机器人可以选择，挑选和转移产品，包装和托盘。另一个例子来自服务机器人技术，其中在人类空间中运作的机器人助手必须经常检索或重新安排多个位置的内在空间，例如架子上的对象。该项目研究哪些多类操纵计划可以有效地在多种型号计划中有效地解决，并在多型摩托学计划中有效地解决了一种功能强大的计算型，并开发了一种强大的计算型。关键的见解是，对于许多现实世界重新者，对象动作的顺序解决了问题，忽略了graving的方面，看起来类似于多体运动计划的解决方案，尤其是对于类似大小的对象。该LinkReveal的研究可以将某些多对象操纵问题作为“图上的卵石运动问题”，这在算法理论和多体运动计划中得到了很好的研究。实行目标是提供严格的方法，并提供多目标操纵的期望完整和最佳保证，这些方法具有良好的可扩展性和效率，这些问题对于问题固有的组合元素的问题面临问题。此类方法也可以用作指导启发式堡垒，并具有其他约束，例如非平凡的动态和不确定性。