# Position and Orientation Based Formation Control of Multiple Rigid   Bodies with Collision Avoidance and Connectivity Maintenance

**Authors:** Christos K. Verginis, Alexandros Nikou, Dimos V. Dimarogonas

arXiv: 1703.08217 · 2017-12-07

## TL;DR

This paper presents a decentralized formation control method for multi-agent systems in 3D space, ensuring formation accuracy, connectivity, and collision avoidance with obstacles using novel potential functions.

## Contribution

It introduces a new decentralized control protocol that maintains connectivity and avoids collisions without requiring detailed agent dynamics knowledge.

## Key findings

- Agents maintain formation with initial neighbors
- Connectivity is preserved throughout the operation
- Collision avoidance with obstacles and workspace boundaries is achieved

## Abstract

This paper addresses the problem of position- and orientation-based formation control of a class of second-order nonlinear multi-agent systems in a $3$D workspace with obstacles. More specifically, we design a decentralized control protocol such that each agent achieves a predefined geometric formation with its initial neighbors, while using local information based on a limited sensing radius. The latter implies that the proposed scheme guarantees that the initially connected agents remain always connected. In addition, by introducing certain distance constraints, we guarantee inter-agent collision avoidance as well as collision avoidance with the obstacles and the boundary of the workspace. The proposed controllers employ a novel class of potential functions and do not require a priori knowledge of the dynamical model, except for gravity-related terms. Finally, simulation results verify the validity of the proposed framework.

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/1703.08217/full.md

## References

36 references — full list in the complete paper: https://tomesphere.com/paper/1703.08217/full.md

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Source: https://tomesphere.com/paper/1703.08217