Autonomous Robotic Navigation Approach Using Deep Q-Network Late Fusion and People Detection-Based Collision Avoidance
Source abstract
Abstract
In this work, we propose an approach for the autonomous navigation of mobile robots using fusion the of sensor data by a Double Deep Q-Network with collision avoidance by detecting moving people via computer vision techniques. We evaluate two data fusion methods for the proposed autonomous navigation approach: Interactive and Late Fusion strategy. Both are used to integrate mobile robot sensors through the following sensors: GPS, IMU, and an RGB-D camera. The proposed collision avoidance module is implemented along with the sensor fusion architecture in order to prevent the autonomous mobile robot from colliding with moving people. The simulation results indicate a significant impact on the success of completing the proposed mission by the mobile robot with the fusion of sensors, indicating a performance increase (success rate) of ≈27% in relation to navigation without sensor fusion. With the addition of moving people in the environment, deploying the people detection and collision avoidance security module has improved about the success rate by 14% when compared to that of the autonomous navigation approach without the security module.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Robotic Path Planning Algorithms · Video Surveillance and Tracking Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Informatics (INF), Federal University of Goias (UFG), Goiás 74690-900, Brazil
School of Electrical, Mechanical and Computer Engineering (EMC), Federal University of Goiás (UFG), Goias 74690-900, Brazil
Funding
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
References (30)
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