Decentralized Nonlinear Model Predictive Control-Based Flock Navigation with Real-Time Obstacle Avoidance in Unknown Obstructed Environments

TL;DR

This paper presents a decentralized NMPC approach for flock navigation that incorporates real-time obstacle avoidance using local sensor data and point cloud processing, validated through simulations and hardware tests.

Contribution

It introduces a novel integration of local obstacle avoidance with NMPC using point clouds, enhancing real-time responsiveness in unknown environments.

Findings

Effective obstacle avoidance with reduced computational load

Successful validation in 3D Gazebo simulations

Feasibility demonstrated via hardware-in-the-loop tests

Abstract

This work extends our prior work on the distributed nonlinear model predictive control (NMPC) for navigating a robot fleet following a certain flocking behavior in unknown obstructed environments with a more realistic local obstacle avoidance strategy. More specifically, we integrate the local obstacle avoidance constraint using point clouds into the NMPC framework. Here, each agent relies on data from its local sensor to perceive and respond to nearby obstacles. A point cloud processing technique is presented for both two-dimensional and three-dimensional point clouds to minimize the computational burden during the optimization. The process consists of directional filtering and down-sampling that significantly reduce the number of data points. The algorithm's performance is validated through realistic 3D simulations in Gazebo, and its practical feasibility is further explored via hardware-in-the-loop (HIL) simulations on embedded platforms.