基于可见性与安全屏障函数的遮挡环境下追逃控制策略
Control Strategies for Pursuit-Evasion Under Occlusion Using Visibility and Safety Barrier Functions
AI总结：
针对遮挡环境下追逃问题中维持逃逸者可见性的难题，提出结合可见性与安全CBF约束、基于采样规划的非短视控制策略，经仿真与实机验证可在强遮挡下仅靠机载传感维持可见性。
摘要：
本文针对存在遮挡的环境中的追逃问题，提出了一种控制策略。我们解决了移动追踪器在视线受阻的情况下，仍能将移动逃逸者保持在其视场（FoV）范围内的难题。研究利用视场的符号距离函数（SDF），将可见性表述为针对追踪器控制输入的控制屏障函数（CBF）约束；同理，基于障碍物集合的符号距离函数（SDF），将避障问题表述为控制屏障函数（CBF）约束。尽管可见性与安全两类控制屏障函数（CBF）满足利普希茨连续性，但它们并非处处可微，因此需要使用广义梯度进行处理。为实现非短视的追踪效果，我们采用基于采样的 kinodynamic 规划器，生成能够实现逃逸者可见性的参考控制轨迹，随后追踪器在控制屏障函数（CBF）约束下通过凸优化跟踪该参考轨迹。我们在CARLA仿真与真实机器人实验中验证了所提方法，结果表明即使在严重遮挡和逃逸者动态运动的情况下，仅依靠机载传感也能成功维持可见性。
英文摘要：
This paper develops a control strategy for pursuit-evasion problems in environments with occlusions. We address the challenge of a mobile pursuer keeping a mobile evader within its field of view (FoV) despite line-of-sight obstructions. The signed distance function (SDF) of the FoV is used to formulate visibility as a control barrier function (CBF) constraint on the pursuer's control inputs. Similarly, obstacle avoidance is formulated as a CBF constraint based on the SDF of the obstacle set. While the visibility and safety CBFs are Lipschitz continuous, they are not differentiable everywhere, necessitating the use of generalized gradients. To achieve non-myopic pursuit, we generate reference control trajectories leading to evader visibility using a sampling-based kinodynamic planner. The pursuer then tracks this reference via convex optimization under the CBF constraints. We validate our approach in CARLA simulations and real-world robot experiments, demonstrating successful visibility maintenance using only onboard sensing, even under severe occlusions and dynamic evader movements.
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