Continuous-Time Trajectory Optimization for Decentralized Multi-Robot Navigation

TL;DR

This paper introduces an online decentralized trajectory replanning algorithm for multi-robot systems, enabling collision-free navigation through continuous-time trajectory optimization with reliable inter-robot communication.

Contribution

It presents a novel online replanning method that uses polynomial-based trajectory generation for decentralized collision avoidance in multi-robot systems.

Findings

Successfully tested in Gazebo simulations with aerial robots.

Achieved collision-free trajectories in dynamic multi-robot scenarios.

Demonstrated real-time applicability of the algorithm.

Abstract

Multi-robot systems have begun to permeate into a variety of different fields, but collision-free navigation in a decentralized manner is still an arduous task. Typically, the navigation of high speed multi-robot systems demands replanning of trajectories to avoid collisions with one another. This paper presents an online replanning algorithm for trajectory optimization in labeled multi-robot scenarios. With reliable communication of states among robots, each robot predicts a smooth continuous-time trajectory for every other remaining robots. Based on the knowledge of these predicted trajectories, each robot then plans a collision-free trajectory for itself. The collision-free trajectory optimization problem is cast as a non linear program (NLP) by exploiting polynomial based trajectory generation. The algorithm was tested in simulations on Gazebo with aerial robots.