# Efficient Informative Sensing using Multiple Robots

arXiv: https://arxiv.org/abs/1401.3462 · HTML: https://mlsanity.com/paper/1401.3462/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

## Built on by (most cited first, 16 of 16)

- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments](https://mlsanity.com/paper/2305.12795/) (2023)
- [Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination](https://mlsanity.com/paper/2209.12429/) (2022)
- [Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning](https://mlsanity.com/paper/2203.02865/) (2022)
- [A Dynamic Programming Algorithm for Finding an Optimal Sequence of Informative Measurements](https://mlsanity.com/paper/2109.11808/) (2021)
- [Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environments](https://mlsanity.com/paper/2310.10863/) (2023)
- [Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh Networks](https://mlsanity.com/paper/2407.10382/) (2024)
- [Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16967/) (2024)
- [Feature Space Exploration For Planning Initial Benthic AUV Surveys](https://mlsanity.com/paper/2105.11598/) (2021)
- [A distributed, plug-n-play algorithm for multi-robot applications with a priori non-computable objective functions](https://mlsanity.com/paper/2111.07441/) (2021)
- [Leveraging Untrustworthy Commands for Multi-Robot Coordination in Unpredictable Environments: A Bandit Submodular Maximization Approach](https://mlsanity.com/paper/2309.16161/) (2023)
- [Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments](https://mlsanity.com/paper/2406.07431/) (2024)
- [Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network Design](https://mlsanity.com/paper/2409.01411/) (2024)
- [Distributed Online Submodular Maximization under Communication Delays: A Simultaneous Decision-Making Approach](https://mlsanity.com/paper/2603.27803/) (2026)
- [RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning](https://mlsanity.com/paper/2607.16745/) (2026)

## Abstract (arXiv)

The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed in-field for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.