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.

翻译：本文针对存在遮挡环境中的追逃问题提出了一种控制策略。我们解决了移动追捕者在视线受阻情况下仍需将移动逃逸者保持在其视野范围内的挑战。通过利用视野的符号距离函数，将可见性构建为追捕者控制输入上的控制屏障函数约束。类似地，基于障碍物集合的符号距离函数将避障构建为CBF约束。虽然可见性与安全性CBF具有利普希茨连续性，但它们并非处处可微，因此需要采用广义梯度方法。为实现非短视追捕，我们通过基于采样的运动动力学规划器生成能够实现逃逸者可见性的参考控制轨迹。随后，追捕者在CBF约束下通过凸优化跟踪该参考轨迹。我们在CARLA仿真环境与真实机器人实验中验证了所提方法，结果表明即使存在严重遮挡和动态逃逸者运动，仅依靠机载传感也能成功维持可见性。