Methods for Collision-Free Navigation of Multiple Mobile Robots in Unknown Cluttered Environments

TL;DR

This paper presents a novel decentralized control framework combining robust model predictive control with reactive local path planning for collision-free navigation of multiple autonomous robots in unknown cluttered environments, considering sensor limitations and disturbance.

Contribution

It introduces an integrated approach that combines robust MPC with reactive navigation, extending to limited sensor data and multi-vehicle coordination with minimal communication.

Findings

Proven collision avoidance under disturbances.

Effective boundary following with limited sensor data.

Decentralized multi-vehicle coordination with single communication exchange.

Abstract

Navigation and guidance of autonomous vehicles is a fundamental problem in robotics, which has attracted intensive research in recent decades. This report is mainly concerned with provable collision avoidance of multiple autonomous vehicles operating in unknown cluttered environments, using reactive decentralized navigation laws, where obstacle information is supplied by some sensor system. Recently, robust and decentralized variants of model predictive control based navigation systems have been applied to vehicle navigation problems. Properties such as provable collision avoidance under disturbance and provable convergence to a target have been shown; however these often require significant computational and communicative capabilities, and don't consider sensor constraints, making real time use somewhat difficult. There also seems to be opportunity to develop a better trade-off between tractability, optimality, and robustness. The main contributions of this work are as follows; firstly, the integration of the robust model predictive control concept with reactive navigation strategies based on local path planning, which is applied to both holonomic and unicycle vehicle models subjected to acceleration bounds and disturbance; secondly, the extension of model predictive control type methods to situations where the information about the obstacle is limited to a discrete ray-based sensor model, for which provably safe, convergent boundary following can be shown; and thirdly the development of novel constraints allowing decentralized coordination of multiple vehicles using a robust model predictive control type approach, where a single communication exchange is used per control update, vehicles are allowed to perform planning simultaneously, and coherency objectives are avoided.