# RLSS: Real-time, Decentralized, Cooperative, Networkless Multi-Robot Trajectory Planning using Linear Spatial Separations

## TL;DR

RLSS is a real-time decentralized multi-robot trajectory planning algorithm that ensures collision avoidance in static environments without requiring communication, demonstrated to outperform existing methods in complex scenarios.

## Contribution

The paper introduces RLSS, a novel decentralized planning algorithm that guarantees collision-free trajectories for multi-robot teams using linear spatial separations and convex optimization.

## Key findings

- RLSS successfully avoids deadlocks and collisions in complex environments.
- The algorithm operates in real-time on physical robots and simulations.
- RLSS outperforms two state-of-the-art planners in empirical tests.

## Abstract

Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots' dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm's performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.