用超网络学习安全区域，让机器人在未知环境里更安全地规划路径。
Learning Maximal Safe Sets Using Hypernetworks for MPC-based Local Trajectory Planning in Unknown Environments
- 用超网络构建神经集合表示，作为MPC的约束条件
- 仿真中成功率提升最高达52%，且运行速度相当
- 适合需要实时安全避障的移动机器人应用
本文提出一种基于学习的新方法，用于在未知静态环境中在线估计局部轨迹规划的最大安全集。将集合的神经表示作为模型预测控制（MPC）局部规划器的终端集约束，提升了递归可行性和安全性。为实现实时性能和良好泛化能力，采用超网络思想。训练过程中以哈密顿-雅可比（HJ）可达性分析为监督信号，支持一般非线性动力学和任意约束。该方法在多种环境和机器人动力学下与基线方法进行广泛仿真对比，结果显示成功率最高提升52%，同时保持相近执行速度。此外，将所提方法NTC-MPC部署于物理机器人，在基线失败场景中仍能安全避障。
原文摘要 · Abstract (English)
This paper presents a novel learning-based approach for online estimation of maximal safe sets for local trajectory planning in unknown static environments. The neural representation of a set is used as the terminal set constraint for a model predictive control (MPC) local planner, resulting in improved recursive feasibility and safety. To achieve real-time performance and desired generalization properties, we employ the idea of hypernetworks. We use the Hamilton-Jacobi (HJ) reachability analysis as the source of supervision during the training process, allowing us to consider general nonlinear dynamics and arbitrary constraints. The proposed method is extensively evaluated against relevant baselines in simulations for different environments and robot dynamics. The results show an increase in success rate of up to 52% compared to the best baseline while maintaining comparable execution speed. Additionally, we deploy our proposed method, NTC-MPC, on a physical robot and demonstrate its ability to safely avoid obstacles in scenarios where the baselines fail.
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