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.

翻译：本研究致力于解决在具有凹形移动障碍物的复杂动态环境中实现安全高效移动机器人导航的挑战。基于控制屏障函数（CBFs）等反应式安全控制器仅依据障碍物当前状态设计避障策略，存在未来碰撞风险。为缓解此问题，我们采用高斯过程在线学习屏障函数，其输入为由基于能量学习的神经网络生成的多模态障碍物运动预测。随后，将学习得到的屏障函数通过调制控制屏障函数（MCBFs）——一种无局部极小值的CBF变体——馈入二次规划问题，以实现安全高效的导航。所提框架作出两项关键贡献：首先，构建了从预测到屏障函数的在线学习流程；其次，引入自主参数调优算法，使MCBFs能够自适应形变的基于预测的屏障函数。该框架在仿真与实物实验中均得到验证，在拥挤动态环境中持续超越基线方法，展现出卓越的安全性与效率。