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Decision Making as Optimization in Multi-robot Teams

机译：作为多机器人团队中的优化决策

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摘要

A key challenge in multi-robot teaming research is deter mining how to properly enable robots to make decisions on actions they should take to contribute to the overall system objective. This article discusses how many forms of decision making in multi-robot teams can be formulated as optimization problems. In particular, we examine the common multi-robot capabilities of task allocation, path planning, for mation generation, and target tracking/observation, showing how each can be represented as optimization problems. Of course, globally opti mal solutions to such formulations are not possible, as it is well-known that such problems are intractable. However, many researchers have suc cessfully built solutions that are approximations to the global problems, which work well in practice. While we do not argue that all decision making in multi-robot systems should be based on optimization formu lations, it is instructive to study when this technique is appropriate. Fu ture development of new approximation algorithms to well-known global optimization problems can therefore have an important positive impact for many applications in multi-robot systems.
机译：多机器人团队研究中的一个关键挑战是，确定如何正确地使机器人做出决定，以决定他们应该采取哪些行动来为整个系统目标做出贡献。本文讨论了可以将多机器人团队中的多种形式的决策制定为优化问题。特别是，我们研究了常见的多机器人功能，如任务分配，路径规划，信息生成和目标跟踪/观察等，展示了如何将每个机器人表示为优化问题。当然，对于这样的制剂，不可能有全球最佳的解决方案，因为众所周知，这样的问题是棘手的。但是，许多研究人员已成功构建了近似于全局问题的解决方案，这些解决方案在实践中效果很好。尽管我们并不认为多机器人系统中的所有决策都应该基于优化公式，但研究何时使用该技术是有启发性的。因此，针对著名的全局优化问题的新的近似算法的未来开发可能会对多机器人系统中的许多应用产生重要的积极影响。

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