An Optimal Control Approach to Mapping GPS-Denied Environments Using a Stochastic Robotic Swarm
An Optimal Control Approach to Mapping GPS-Denied Environments Using a Stochastic Robotic Swarm
复制标题
使用随机机器人群绘制 GPS 受限环境的最佳控制方法
DOI：
10.1007/978-3-319-51532-8_29
复制
发表时间：
2015
影响因子：
37.4
通讯作者：
S. Berman
中科院分区：
文献类型：
--
作者：
R. Ramachandran;Karthik Elamvazhuthi;S. Berman
本文提出了一种使用机器人群的观察结果来绘制感兴趣区域的方法，无需进行定位。这些机器人具有本地传感能力，但没有通信能力，并且它们的运动表现出随机性。我们使用一组平流扩散反应偏微分方程（PDE）对群体种群动态进行建模。使用空间相关的指示函数将环境地图合并到该模型中，该函数标记整个域中感兴趣区域的存在或不存在。为了估计这个指示函数，我们将其定义为优化问题的解决方案，其中我们最小化基于时间机器人数据的目标函数。使用标准梯度下降算法以离线数值方式执行优化。模拟表明，我们的方法可以对未知环境中不同类型区域的位置和几何形状进行相当准确的估计。
This paper presents an approach to mapping a region of interest using observations from a robotic swarm without localization. The robots have local sensing capabilities and no communication, and they exhibit stochasticity in their motion. We model the swarm population dynamics with a set of advection-diffusion-reaction partial differential equations (PDEs). The map of the environment is incorporated into this model using a spatially-dependent indicator function that marks the presence or absence of the region of interest throughout the domain. To estimate this indicator function, we define it as the solution of an optimization problem in which we minimize an objective functional that is based on temporal robot data. The optimization is performed numerically offline using a standard gradient descent algorithm. Simulations show that our approach can produce fairly accurate estimates of the positions and geometries of different types of regions in an unknown environment.