Hybrid Multi-camera Visual Servoing to Moving Target

TL;DR

This paper introduces a hybrid multi-camera visual servoing system that effectively guides a robot arm to moving targets despite occlusions by dynamically switching between multiple RGBD sensors and an arm-mounted stereo camera.

Contribution

It presents a novel adaptive visual servoing approach combining Eye-to-Hand and Eye-in-Hand sensors with a master controller for robust target tracking in dynamic and occluded environments.

Findings

Successfully tracked moving targets in various scenarios

Demonstrated robustness against occlusions and sensor view limitations

Achieved accurate and adaptive robot guidance in real-time

Abstract

Visual servoing is a well-known task in robotics. However, there are still challenges when multiple visual sources are combined to accurately guide the robot or occlusions appear. In this paper we present a novel visual servoing approach using hybrid multi-camera input data to lead a robot arm accurately to dynamically moving target points in the presence of partial occlusions. The approach uses four RGBD sensors as Eye-to-Hand (EtoH) visual input, and an arm-mounted stereo camera as Eye-in-Hand (EinH). A Master supervisor task selects between using the EtoH or the EinH, depending on the distance between the robot and target. The Master also selects the subset of EtoH cameras that best perceive the target. When the EinH sensor is used, if the target becomes occluded or goes out of the sensor's view-frustum, the Master switches back to the EtoH sensors to re-track the object. Using this adaptive visual input data, the robot is then controlled using an iterative planner that uses position, orientation and joint configuration to estimate the trajectory. Since the target is dynamic, this trajectory is updated every time-step. Experiments show good performance in four different situations: tracking a ball, targeting a bulls-eye, guiding a straw to a mouth and delivering an item to a moving hand. The experiments cover both simple situations such as a ball that is mostly visible from all cameras, and more complex situations such as the mouth which is partially occluded from some of the sensors.