Probabilistically Robust Trajectory Planning of Multiple Aerial Agents

TL;DR

This paper presents a novel distributed control method for autonomous aerial agents that robustly plans safe trajectories under complex, non-Gaussian uncertainties, overcoming limitations of traditional Gaussian-based approaches.

Contribution

It introduces a probabilistically robust distributed controller using exact uncertainty propagation for nonlinear systems with non-Gaussian disturbances.

Findings

Successfully handles non-Gaussian uncertainties

Ensures safety and robustness in complex scenarios

Integrates into standard MPC frameworks

Abstract

Current research on robust trajectory planning for autonomous agents aims to mitigate uncertainties arising from disturbances and modeling errors while ensuring guaranteed safety. Existing methods primarily utilize stochastic optimal control techniques with chance constraints to maintain a minimum distance among agents with a guaranteed probability. However, these approaches face challenges, such as the use of simplifying assumptions that result in linear system models or Gaussian disturbances, which limit their practicality in complex realistic scenarios. To address these limitations, this work introduces a novel probabilistically robust distributed controller enabling autonomous agents to plan safe trajectories, even under non-Gaussian uncertainty and nonlinear systems. Leveraging exact uncertainty propagation techniques based on mixed-trigonometric-polynomial moment propagation, this method transforms non-Gaussian chance constraints into deterministic ones, seamlessly integrating them into a distributed model predictive control framework solvable with standard optimization tools. Simulation results demonstrate the effectiveness of this technique, highlighting its ability to consistently handle various types of uncertainty, ensuring robust and accurate path planning in complex scenarios.