# Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects

arXiv: https://arxiv.org/abs/2203.04551 · HTML: https://mlsanity.com/paper/2203.04551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents](https://mlsanity.com/paper/2401.03154/) (2024)
- [Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing](https://mlsanity.com/paper/2506.09469/) (2025)

## Abstract (arXiv)

We consider the online planning problem for a team of agents to discover and track an unknown and time-varying number of moving objects from onboard sensor measurements with uncertain measurement-object origins. Since the onboard sensors have limited field-of-views, the usual planning strategy based solely on either tracking detected objects or discovering unseen objects is inadequate. To address this, we formulate a new information-based multi-objective multi-agent control problem, cast as a partially observable Markov decision process (POMDP). The resulting multi-agent planning problem is exponentially complex due to the unknown data association between objects and multi-sensor measurements; hence, computing an optimal control action is intractable. We prove that the proposed multi-objective value function is a monotone submodular set function, which admits low-cost suboptimal solutions via greedy search with a tight optimality bound. The resulting planning algorithm has a linear complexity in the number of objects and measurements across the sensors, and quadratic in the number of agents. We demonstrate the proposed solution via a series of numerical experiments with a real-world dataset.