A gradient optimization approach to adaptive multi-robot control
A gradient optimization approach to adaptive multi-robot control
复制标题
自适应多机器人控制的梯度优化方法
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
M. Schwager
中科院分区：
文献类型：
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作者：
D. Rus;M. Schwager
本文提出了一种统一的方法来控制一组机器人以分散的方式达到目标配置。作为一个激励的例子，机器人被控制在一个环境中展开，以提供传感器覆盖。这个例子产生了一个成本函数，它被证明具有惊人的普遍性。通过改变单个自由参数，成本函数捕获了以前被视为不相关的各种不同的多机器人目标。稳定的分布式控制器是通过取这个代价函数的梯度生成的。基于底层成本函数的凸性，描述了两类基本的多机器人行为。凸代价函数导致共识（所有机器人移动到相同的位置），而任何其他行为都需要非凸代价函数。
This thesis proposes a unified approach for controlling a group of robots to reach a goal configuration in a decentralized fashion. As a motivating example, robots are controlled to spread out over an environment to provide sensor coverage. This example gives rise to a cost function that is shown to be of a surprisingly general nature. By changing a single free parameter, the cost function captures a variety of different multi-robot objectives which were previously seen as unrelated. Stable, distributed controllers are generated by taking the gradient of this cost function. Two fundamental classes of multi-robot behaviors are delineated based on the convexity of the underlying cost function. Convex cost functions lead to consensus (all robots move to the same position), while any other behavior requires a nonconvex cost function.
The multi-robot controllers are then augmented with a stable on-line learning mechanism to adapt to unknown features in the environment. In a sensor coverage application, this allows robots to learn where in the environment they are most needed, and to aggregate in those areas. The learning mechanism uses communication between neighboring robots to enable distributed learning over the multi-robot system in a provably convergent way.
Three multi-robot controllers are then implemented on three different robot platforms. Firstly, a controller for deploying robots in an environment to provide sensor coverage is implemented on a group of 16 mobile robots. They learn to aggregate around a light source while covering the environment. Secondly, a controller is implemented for deploying a group of three flying robots with downward facing cameras to monitor an environment on the ground. Thirdly, the multi-robot model is used as a basis for modeling the behavior of a herd of cows using a system identification approach. The controllers in this thesis are distributed, theoretically proven, and implemented on multi-robot platforms. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)