Occlusion-Free Image Based Visual Servoing using Probabilistic Control Barrier Certificates

TL;DR

This paper introduces a probabilistic control barrier function approach integrated with MPC to ensure occlusion-free visual servoing in robotics, effectively handling measurement noise and obstacle avoidance.

Contribution

It develops Probabilistic Control Barrier Certificates that encode chance-constrained occlusion avoidance, enhancing IBVS robustness under uncertainty.

Findings

Successfully maintains feature points in view despite occlusions.

Ensures occlusion avoidance with predefined probability levels.

Validates approach through simulation results.

Abstract

Image-based visual servoing (IBVS) is a widely-used approach in robotics that employs visual information to guide robots towards desired positions. However, occlusions in this approach can lead to visual servoing failure and degrade the control performance due to the obstructed vision feature points that are essential for providing visual feedback. In this paper, we propose a Control Barrier Function (CBF) based controller that enables occlusion-free IBVS tasks by automatically adjusting the robot's configuration to keep the feature points in the field of view and away from obstacles. In particular, to account for measurement noise of the feature points, we develop the Probabilistic Control Barrier Certificates (PrCBC) using control barrier functions that encode the chance-constrained occlusion avoidance constraints under uncertainty into deterministic admissible control space for the robot, from which the resulting configuration of robot ensures that the feature points stay occlusion free from obstacles with a satisfying predefined probability. By integrating such constraints with a Model Predictive Control (MPC) framework, the sequence of optimized control inputs can be derived to achieve the primary IBVS task while enforcing the occlusion avoidance during robot movements. Simulation results are provided to validate the performance of our proposed method.