# Multi-objective Compositions for Collision-Free Connectivity Maintenance in Teams of Mobile Robots

arXiv: https://arxiv.org/abs/1608.06887 · HTML: https://mlsanity.com/paper/1608.06887/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2016.

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

- [Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments](https://mlsanity.com/paper/2201.01347/) (2022)
- [Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance](https://mlsanity.com/paper/2307.03015/) (2023)
- [Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddings](https://mlsanity.com/paper/2403.10497/) (2024)
- [Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach](https://mlsanity.com/paper/2109.08536/) (2021)
- [Barrier Function Overrides For Non-Convex Fixed Wing Flight Control and Self-Driving Cars](https://mlsanity.com/paper/2505.05548/) (2025)

## Abstract (arXiv)

Compositional barrier functions are proposed in this paper to systematically compose multiple objectives for teams of mobile robots. The objectives are first encoded as barrier functions, and then composed using AND and OR logical operators. The advantage of this approach is that compositional barrier functions can provably guarantee the simultaneous satisfaction of all composed objectives. The compositional barrier functions are applied to the example of ensuring collision avoidance and static/dynamical graph connectivity of teams of mobile robots. The resulting composite safety and connectivity barrier certificates are verified experimentally on a team of four mobile robots.