# Information Acquisition with Sensing Robots: Algorithms and Error Bounds

arXiv: https://arxiv.org/abs/1309.5390 · HTML: https://mlsanity.com/paper/1309.5390/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2013.

## Built on by (most cited first, 5 of 5)

- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories](https://mlsanity.com/paper/2212.01498/) (2022)
- [Constrained Bayesian Experimental Design via Online Planning](https://mlsanity.com/paper/2605.26990/) (2026)
- [Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression](https://mlsanity.com/paper/2203.03828/) (2022)
- [Trajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization](https://mlsanity.com/paper/2606.09188/) (2026)

## Abstract (arXiv)

Utilizing the capabilities of configurable sensing systems requires addressing difficult information gathering problems. Near-optimal approaches exist for sensing systems without internal states. However, when it comes to optimizing the trajectories of mobile sensors the solutions are often greedy and rarely provide performance guarantees. Notably, under linear Gaussian assumptions, the problem becomes deterministic and can be solved off-line. Approaches based on submodularity have been applied by ignoring the sensor dynamics and greedily selecting informative locations in the environment. This paper presents a non-greedy algorithm with suboptimality guarantees, which does not rely on submodularity and takes the sensor dynamics into account. Our method performs provably better than the widely used greedy one. Coupled with linearization and model predictive control, it can be used to generate adaptive policies for mobile sensors with non-linear sensing models. Applications in gas concentration mapping and target tracking are presented.