Towards Scalable Continuous-Time Trajectory Optimization for Multi-Robot Navigation

TL;DR

This paper presents a decentralized, computationally efficient continuous-time trajectory optimization algorithm for multi-robot navigation, enabling scalable coordination with minimal communication and real-time collision avoidance.

Contribution

It introduces a novel decentralized approach using model predictive control and optimal control primitives, optimized for scalability and efficiency in multi-robot systems.

Findings

Efficient for up to 40 homogeneous robots and 21 heterogeneous robots.

Operates with minimal communication by sharing only current states and goals.

Achieves real-time collision avoidance through a condensed NLP formulation.

Abstract

Scalable multi-robot transition is essential for ubiquitous adoption of robots. As a step towards it, a computationally efficient decentralized algorithm for continuous-time trajectory optimization in multi-robot scenarios based upon model predictive control is introduced. The robots communicate only their current states and goals rather than sharing their whole trajectory; using this data each robot predicts a continuous-time trajectory for every other robot exploiting optimal control based motion primitives that are corrected for spatial inter-robot interactions using least squares. A non linear program (NLP) is formulated for collision avoidance with the predicted trajectories of other robots. The NLP is condensed by using time as a parametrization resulting in an unconstrained optimization problem and can be solved in a fast and efficient manner. Additionally, the algorithm resizes the robot to accommodate it's trajectory tracking error. The algorithm was tested in simulations on Gazebo with aerial robots. Early results indicate that the proposed algorithm is efficient for upto forty homogeneous robots and twenty one heterogeneous robots occupying 20\% of the available space.