Control Strategies for Pursuit-Evasion Under Occlusion Using Visibility and Safety Barrier Functions

2024-11-02

arXiv.org

AI Summary

This work addresses the challenge of maintaining visual visibility of a dynamic evader under occlusion-prone environments for pursuers. We propose a robust control strategy that jointly enforces visibility preservation and obstacle avoidance. Our key contribution is the first formulation of line-of-sight visibility as a nonsmooth control barrier function (CBF) grounded in the signed distance function (SDF), integrated with generalized gradient theory and sampling-based motion planning to achieve safe, tight visibility constraint optimization without myopic assumptions. The method operates solely on onboard sensor data and realizes real-time trajectory tracking and decision-making via convex optimization. Evaluations in CARLA simulation and on physical robot platforms demonstrate stable visibility maintenance under severe occlusions and complex evader dynamics, significantly enhancing the robustness and practicality of multi-agent visual tracking.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Develops control strategy for pursuit-evasion with occlusions
• Maintains evader visibility using barrier functions and gradients
• Validates approach in simulations and real-world robot experiments

Innovation

• Uses visibility CBF for occlusion handling
• Employs sampling-based kinodynamic planner
• Tracks trajectories via convex optimization