多智能体集群在未知障碍环境中实时避障的分布式非线性预测控制方法
Decentralized Nonlinear Model Predictive Control-Based Flock Navigation with Real-Time Obstacle Avoidance in Unknown Obstructed Environments
- 基于点云数据构建局部避障约束，融入非线性模型预测控制框架
- 通过方向滤波与降采样显著降低计算负担，点数减少超70%
- 支持真实3D仿真与嵌入式硬件在环验证，适合无人机/机器人集群应用
本文扩展了此前关于分布式非线性模型预测控制（NMPC）在未知障碍环境中实现机器人集群群体行为导航的研究，引入更贴近实际的局部避障策略。具体而言，将基于点云的局部避障约束集成到NMPC框架中，每个智能体依赖自身传感器感知并响应附近障碍物。提出一种适用于二维与三维点云的处理技术，通过方向滤波和降采样显著减少优化过程中的数据量。算法性能在Gazebo中的真实3D仿真中得到验证，并通过嵌入式平台的软硬件协同仿真进一步评估其实际可行性。
原文摘要 · Abstract (English)
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.
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