Trajectory Optimization for Adaptive Informative Path Planning with Multimodal Sensing

TL;DR

This paper presents a trajectory optimization method for autonomous agents with multimodal sensors to efficiently explore environments, significantly reducing uncertainty and error within resource constraints, applicable to planetary rover missions.

Contribution

It introduces a projection-based trajectory optimization approach that effectively manages multiple sensors and resource constraints for informative path planning in unknown environments.

Findings

Achieves up to 85% variance reduction in environment belief.

Reduces root-mean square error by 50%.

Outperforms previous methods in long horizon planning.

Abstract

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.