# Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning

## TL;DR

This paper introduces an online decentralized trajectory optimization method for multi-robot systems operating in unknown environments, enabling collision-free navigation through local mapping and inter-robot communication.

## Contribution

It presents a novel decentralized trajectory planning algorithm that accounts for dynamic obstacles and robot interactions using local maps and current state communication.

## Key findings

- Successfully tested in Gazebo simulations with ROS
- Achieves collision-free trajectories in unknown environments
- Utilizes local object-based maps and inter-robot communication

## Abstract

Multi robot systems have the potential to be utilized in a variety of applications. In most of the previous works, the trajectory generation for multi robot systems is implemented in known environments. To overcome that we present an online trajectory optimization algorithm that utilizes communication of robots' current states to account to the other robots while using local object based maps for identifying obstacles. Based upon this data, we predict the trajectory expected to be traversed by the robots and utilize that to avoid collisions by formulating regions of free space that the robot can be without colliding with other robots and obstacles. A trajectory is optimized constraining the robot to remain within this region.The proposed method is tested in simulations on Gazebo using ROS.