Attention-based Active Visual Search for Mobile Robots
This work addresses the challenge of efficient object search for mobile robots in complex environments, representing an incremental improvement over existing reactive or simplified sensor-based strategies.
The paper tackles the problem of active visual search for objects in unknown environments using mobile robots, proposing an algorithm that integrates visual attention techniques with non-myopic decision-making to guide robots toward more relevant areas, resulting in performance improvements of up to 42% in structured and 38% in unstructured environments.
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.