Attention-based Active Visual Search for Mobile Robots

TL;DR

This paper introduces an active visual search model for mobile robots that leverages visual attention techniques and non-myopic decision-making to improve search efficiency in unknown environments.

Contribution

It proposes a novel attention-based active search algorithm combining top-down and bottom-up models, enhancing search performance over existing strategies.

Findings

Up to 42% improvement in structured environments

Up to 38% improvement in cluttered environments

Visual attention significantly enhances search effectiveness

Abstract

We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli provided by the visual sensors. Existing search strategies are either purely reactive or use simplified sensor models that do not exploit all the visual information available. In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search more relevant areas of the environment. The attention module couples both top-down and bottom-up attention models enabling the robot to search regions with higher importance first. The proposed algorithm is evaluated on a mobile robot platform in a 3D simulated environment. The results indicate that the use of visual attention significantly improves search, but the degree of improvement depends on the nature of the task and the complexity of the environment. In our experiments, we found that performance enhancements of up to 42\% in structured and 38\% in highly unstructured cluttered environments can be achieved using visual attention mechanisms.