Munir Aiman, Parasuraman Ramviyas
School of Computing, University of Georgia, Athens, GA 30602, USA.
Sensors (Basel). 2023 Dec 4;23(23):9600. doi: 10.3390/s23239600.
Adaptive information-sampling approaches enable efficient selection of mobile robots' waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot's energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm's information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point's informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map's accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable.
自适应信息采样方法能够有效地选择移动机器人的航路点，通过这些航路点可以获得对诸如辐射或场强等物理过程的精确感知和测绘。信息采样目标函数中的一个关键参数可以进行优化，以平衡在不确定性非常高的地方探索新信息的需求与利用迄今采样的数据的需求，利用这些数据可以获得大量潜在的空间场，例如物理过程的源位置或模式。然而，文献中的研究要么假设机器人的能量不受限制，要么使用不同机器人之间能量容量的均匀可用性。因此，本文分析了自适应信息采样算法的信息函数在探索和利用中所起的作用，以在平衡测绘、定位和能量效率目标之间实现权衡。我们使用高斯过程回归（GPR）来预测和估计置信区间，从而确定每个点的信息量。通过大量的实验数据，我们对信息函数参数在单机器人和多机器人场景中对预测地图的准确性（均方根误差）、置信区间（方差）、能量消耗（距离）和花费时间（样本数量）的影响提供了更深入和全面的视角。结果为基于传感目标（例如源定位或测绘）选择合适的能量感知信息函数参数提供了有意义的见解。基于我们的分析，我们可以得出结论，仅重视信息函数的不确定性（这会使能量需求激增）或信息的预测均值（这会损害测绘精度）都是有害的。通过对信息不确定性赋予更大的权重，同时对信息值赋予一定的非零权重（例如75:25的比例）,可以在探索和利用目标之间实现最佳权衡，同时使能量需求可控。
