Gerdpratoom Nuthasith, Yamamoto Kaoru
Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
Front Robot AI. 2025 Jun 10;12:1540808. doi: 10.3389/frobt.2025.1540808. eCollection 2025.
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.
这项工作扩展了我们之前关于分布式非线性模型预测控制（NMPC）的研究，该控制用于在未知障碍物环境中引导机器人编队遵循特定的群聚行为，并采用了更现实的局部避障策略。更具体地说，我们将使用点云的局部避障约束集成到NMPC框架中。在这里，每个智能体依靠其本地传感器的数据来感知并响应附近的障碍物。针对二维和三维点云都提出了一种点云处理技术，以在优化过程中最小化计算负担。该过程包括方向滤波和下采样，可显著减少数据点的数量。通过在Gazebo中进行逼真的3D模拟验证了算法的性能，并通过在嵌入式平台上进行硬件在环（HIL）模拟进一步探索了其实际可行性。结果表明，智能体能够在有障碍物的环境中安全导航，并且HIL模拟证实了在嵌入式计算机上部署该方案的可行性。这些结果表明，所提出的NMPC方案适用于在复杂环境中运行的分散式机器人系统中的实际机器人部署。
