Value-based action selection for observation with robot teams using probabilistic techniques
Value-based action selection for observation with robot teams using probabilistic techniques
复制标题
使用概率技术进行机器人团队观察的基于价值的动作选择
DOI：
10.1016/j.robot.2004.08.002
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
T. Balch
中科院分区：
文献类型：
--
作者：
A. Stroupe;T. Balch
我们提出了一种方法，用于指导下一步的机器人团队从事映射对象在他们的环境中的运动：移动机器人团队的价值估计（MVERT）。由此产生的机器人路径往往通过最大化信息增益来优化团队中所有机器人的Vantage位置。在每一步中，每个机器人都选择一个动作来最大化其下一次观察的效用（在这种情况下，减少不确定性）。轨迹不能保证是最优的，但团队行为可以最大限度地提高团队的知识，因为每个机器人都会考虑队友的观察贡献。MVERT在模拟中进行评估，通过测量所得到的不确定性目标位置相比，由机器人采取行动，而不考虑队友的位置和全局优化的所有机器人的每一个步骤。此外，MVERT还在机器人的物理团队上进行了演示。团队的定性行为是适当的，接近单步最优轨迹集。
We present an approach for directing next-step movements of robot teams engaged in mapping objects in their environment: Move Value Estimation for Robot Teams (MVERT). Resulting robot paths tend to optimize vantage points for all robots on the team by maximizing information gain. At each step, each robot selects a movement to maximize the utility (in this case, reduction in uncertainty) of its next observation. Trajectories are not guaranteed to be optimal, but team behavior serves to maximize the team's knowledge since each robot considers the observational contributions of team mates. MVERT is evaluated in simulation by measuring the resulting uncertainty about target locations compared to that obtained by robots acting without regard to team mate locations and to that of global optimization over all robots for each single step. Additionally, MVERT is demonstrated on physical teams of robots. The qualitative behavior of the team is appropriate and close to the single-step optimal set of trajectories.