One Robot for Many Tasks: Versatile Co-Design Through Stochastic Programming
One Robot for Many Tasks: Versatile Co-Design Through Stochastic Programming
复制标题
一台机器人完成多项任务：通过随机编程进行多功能协同设计
DOI：
10.1109/lra.2020.2969948
复制
发表时间：
2020
影响因子：
5.2
通讯作者：
Patrick M. Wensing
中科院分区：
文献类型：
--
作者：
Gabriel Bravo;A. Prete;Patrick M. Wensing
多功能性是推动机器人在装配线和其他应用中采用的主要因素之一。与固定自动化解决方案相比，单个工业机器人可以执行广泛的任务（例如，焊接、提升）。在其他平台中，如腿式机器人，多功能性是适应各种地形的必要条件。在这些预期场景中平衡性能的能力是设计过程的主要挑战之一。为了应对这一挑战，这封信提出了一个新的框架，通过考虑多个任务和环境中的机械设计和控制之间的相互作用，计算设计的多功能机器人。所提出的方法优化形态参数，同时调整控制参数，使用轨迹优化（TO），使一个单一的设计可以完成多个任务。作为其主要贡献，这封信详细介绍了一种方法，联合收割机方法从随机规划（SP）与TO来解决这些多任务协同设计问题的可扩展性。为了评估这一贡献的影响，这封信考虑的问题，设计一个平面机械手运输范围内的负载和跳跃的单足机器人，必须跨越各种地形跳跃。所提出的配方实现更快的解决方案的时间和改进的可扩展性相比，最先进的协同设计解决方案。由此产生的设计也被证明是更通用的，在提供更好的能源成本和跨多个场景的任务完成时间。
Versatility is one of the main factors driving the adoption of robots on the assembly line and in other applications. Compared to fixed-automation solutions, a single industrial robot can perform a wide range of tasks (e.g., welding, lifting). In other platforms, such as legged robots, versatility is a necessity to negotiate varied terrains. The ability to balance performance across these anticipated scenarios is one of the main challenges to the process of design. To address this challenge, this letter proposes a new framework for the computational design of versatile robots by considering the interplay between mechanical design and control across multiple tasks and environments. The proposed method optimizes morphology parameters while simultaneously adjusting control parameters using trajectory optimization (TO) so that a single design can fulfill multiple tasks. As its main contribution, the letter details an approach to combine methods from stochastic programming (SP) with TO to address the scalability of these multi-task co-design problems. To assess the effects of this contribution, this letter considers the problems of designing a planar manipulator to transport a range of loads and a hopping monopod robot that must jump across a variety of terrains. The proposed formulation achieves faster solution times and improved scalability in comparison to state of the art co-design solutions. The resulting designs are also shown to be more versatile in terms of providing improved energy cost and task completion times across multiple scenarios.