用动态高斯过程让机器人实时避障更安全顺滑
Dynamic Log-Gaussian Process Control Barrier Function for Safe Robotic Navigation in Dynamic Environments
- 用对数变换的高斯过程生成平滑障碍物屏障值
- 融合障碍物位置与速度预测，提前响应动态变化
- 适合需要实时避障的移动机器人系统
控制屏障函数（CBF）已成为解决机器人安全导航问题的有效工具。然而，在未知且动态的环境中，基于实时传感器数据在线合成具有信息量且感知障碍物运动的CBF仍具挑战性。为此，本文提出一种基于高斯过程的新式CBF——动态对数高斯过程控制屏障函数（DLGP-CBF），实现空间信息丰富且对障碍物运动敏感的实时屏障构建。首先，通过高斯过程回归的对数变换，即使在数据稀疏区域也能生成平滑、富有信息量的屏障值与梯度；其次，将DLGP-CBF显式建模为障碍物位置的函数，所导出的安全约束融入了障碍物速度预测，使控制器能主动响应动态障碍物运动。仿真结果表明，相比基线方法，该方法在避障性能上显著提升，表现为更高的安全裕度、更平滑的轨迹以及更强的响应能力。
原文摘要 · Abstract (English)
Control Barrier Functions (CBFs) have emerged as efficient tools to address the safe navigation problem for robot applications. However, synthesizing informative and obstacle motion-aware CBFs online using real-time sensor data remains challenging, particularly in unknown and dynamic scenarios. Motived by this challenge, this paper aims to propose a novel Gaussian Process-based formulation of CBF, termed the Dynamic Log Gaussian Process Control Barrier Function (DLGP-CBF), to enable real-time construction of CBF which are both spatially informative and responsive to obstacle motion. Firstly, the DLGP-CBF leverages a logarithmic transformation of GP regression to generate smooth and informative barrier values and gradients, even in sparse-data regions. Secondly, by explicitly modeling the DLGP-CBF as a function of obstacle positions, the derived safety constraint integrates predicted obstacle velocities, allowing the controller to proactively respond to dynamic obstacles' motion. Simulation results demonstrate significant improvements in obstacle avoidance performance, including increased safety margins, smoother trajectories, and enhanced responsiveness compared to baseline methods.
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