Decentralized nonlinear model predictive control-based flock navigation with real-time obstacle avoidance in unknown obstructed environments.

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. The results demonstrate that the agents can safely navigate through obstructed environments, and the HIL simulation confirms the feasibility of deploying this scheme on an embedded computer. These results suggest that the proposed NMPC scheme is suitable for real-world robotics deployment in decentralized robotic systems operating in complex environments.