# Spatially temporally distributed informative path planning for multi-robot systems

arXiv: https://arxiv.org/abs/2403.16489 · HTML: https://mlsanity.com/paper/2403.16489/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization](https://mlsanity.com/paper/2604.13179/) (2026)

## Abstract (arXiv)

This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian Process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of interest. To spatially and temporally navigate the group of robots so that they can optimally acquire maximal information gains while their connectivity is preserved, we propose a novel multistep prediction informative path planning optimization strategy employing our newly defined local cost functions. By using the dual decomposition method, it is feasible and practical to effectively solve the optimization problem in a distributed manner. The proposed method was validated through synthetic experiments utilizing real-world data sets.