用预测障碍物轨迹来实时生成安全导航屏障，避免碰撞。
Proactive Local-Minima-Free Robot Navigation: Blending Motion Prediction with Safe Control
- 用神经网络预测障碍物运动，通过高斯过程学习动态安全屏障。
- 在复杂动态场景中实现零局部极小值的高效安全导航。
- 适合需要高安全性的机器人导航任务，如人机共存环境。
本文针对复杂动态环境中带有凹形移动障碍物的移动机器人安全高效导航问题提出解决方案。传统反应式安全控制器（如控制屏障函数，CBFs）仅基于障碍物当前状态设计避障策略，存在未来碰撞风险。为此，我们利用高斯过程，从基于能量学习训练的神经网络生成的多模态运动预测中，在线学习屏障函数。所学屏障函数被输入到采用调制控制屏障函数（MCBFs）的二次规划框架中，该方法无局部极小值，可实现安全高效的导航。本框架贡献两点：一是构建了从预测到屏障函数的在线学习流程；二是提出自主参数调节算法，使MCBF能适应随时间变化的、基于预测的屏障函数。在仿真与真实实验中，该方法持续优于基线模型，在拥挤动态环境中展现出更优的安全性与效率。
原文摘要 · Abstract (English)
This work addresses the challenge of safe and efficient mobile robot navigation in complex dynamic environments with concave moving obstacles. Reactive safe controllers like Control Barrier Functions (CBFs) design obstacle avoidance strategies based only on the current states of the obstacles, risking future collisions. To alleviate this problem, we use Gaussian processes to learn barrier functions online from multimodal motion predictions of obstacles generated by neural networks trained with energy-based learning. The learned barrier functions are then fed into quadratic programs using modulated CBFs (MCBFs), a local-minimum-free version of CBFs, to achieve safe and efficient navigation. The proposed framework makes two key contributions. First, it develops a prediction-to-barrier function online learning pipeline. Second, it introduces an autonomous parameter tuning algorithm that adapts MCBFs to deforming, prediction-based barrier functions. The framework is evaluated in both simulations and real-world experiments, consistently outperforming baselines and demonstrating superior safety and efficiency in crowded dynamic environments.
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