用视觉与安全约束实现遮挡环境下的追逃控制
Control Strategies for Pursuit-Evasion Under Occlusion Using Visibility and Safety Barrier Functions
- 将视野与避障转化为控制屏障函数，约束追击者动作
- 通过采样规划生成预瞄轨迹，实现非短视追击
- 实机与仿真验证有效，仅靠车载传感器即可
本文针对存在遮挡的追逃问题提出控制策略。为使追击者在视线受阻时仍能保持对逃逸者的可视，利用视野的符号距离函数（SDF）构建可见性控制屏障函数（CBF），作为对追击者控制输入的约束；同样，基于障碍物SDF构建安全CBF以避免碰撞。尽管可见性与安全CBF均为Lipschitz连续但非处处可导，需采用广义梯度求解。为实现非短视追击，采用基于采样的运动学动力学规划器生成导向可视性的参考控制轨迹，追击者通过凸优化在此轨迹上跟踪，并满足CBF约束。在CARLA仿真和真实机器人实验中验证了该方法的有效性，即使在严重遮挡和动态逃逸者情况下，也仅依靠车载传感实现了持续可视性维持。
原文摘要 · Abstract (English)
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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