We consider the problem of an autonomous agent equipped with multiple sensors, each with different sensing precision and energy costs. The agent's goal is to explore the environment and gather information subject to its resource constraints in unknown, partially observable environments. The challenge lies in reasoning about the effects of sensing and movement while respecting the agent's resource and dynamic constraints. We formulate the problem as a trajectory optimization problem and solve it using a projection-based trajectory optimization approach where the objective is to reduce the variance of the Gaussian process world belief. Our approach outperforms previous approaches in long horizon trajectories by achieving an overall variance reduction of up to 85% and reducing the root-mean square error in the environment belief by 50%. This approach was developed in support of rover path planning for the NASA VIPER Mission.

翻译：我们考虑一个配备多种传感器的自主智能体问题，每种传感器具有不同的感知精度和能量成本。智能体的目标是在未知、部分可观测的环境中，在资源约束条件下探索环境并收集信息。其挑战在于在尊重智能体资源与动力学约束的同时，对感知与运动的影响进行推理。我们将该问题形式化为一个轨迹优化问题，并采用基于投影的轨迹优化方法进行求解，其优化目标为降低高斯过程世界置信度的方差。我们的方法在长时域轨迹规划中显著优于先前方法，实现了高达85%的总体方差缩减，并将环境置信度的均方根误差降低了50%。该方法是针对NASA VIPER任务中月球车路径规划需求开发的。