避障
避碰
计算机科学
控制理论(社会学)
车辆动力学
障碍物
功能(生物学)
跟踪(教育)
路径(计算)
控制(管理)
移动机器人
控制工程
工程类
汽车工程
人工智能
机器人
计算机安全
心理学
教育学
程序设计语言
进化生物学
法学
政治学
生物
碰撞
作者
Jinheng Han,Junzhi Zhang,Chengkun He,Chen Lv,Henglai Wei,Junfeng Zhang,Shiyue Zhao
标识
DOI:10.1109/tits.2024.3384444
摘要
Precise path tracking and agilely avoiding obstacles are essential for the stability and safety of autonomous driving. In this paper, we introduce a uniform safe path tracking control strategy that combines obstacle avoidance with path tracking via a barrier function. Unlike the conventional hierarchical collision avoidance methods, our approach employs an integral heuristic barrier function that addresses obstacle avoidance planning and reference trajectory tracking problems simultaneously. Via this, the complex safe trajectory following problem is simplified into a tractable yaw angle tracking problem. We then present a novel finite-time adaptive barrier function-based sliding mode controller that handles input saturation and enhances robustness. This ensures precise and robust yaw angle tracking within specified performance constraints. Moreover, the proposed approach achieves accelerated finite-time convergence compared to the exponential convergence rate. Finally, the Carsim-Simulink co-simulations and real-vehicle experiments validate the effectiveness and superiority of our method in addressing the path-tracking challenge, while upholding driving safety.
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