Simulation and Modeling of Control and Sensorization Strategies for Autonomous Navigation with Differential Drive Robots: Unknown Environments Focused Approach
Full paper published at LACCEI 2024
Luiz Gustavo de Lima, Universidade São Francisco, Brazil (lglima61@gmail.com)
Vicente Idalberto Becerra Sablón, Universidade São Francisco, Brazil (vicente.sablon@usf.edu.br)
The objective of this article is to present the modeling and simulation of control and sensorization strategies applied to autonomous navigation of a differential drive robot, with the purpose of planning a trajectory in an unknown environment. It explores the growing impact of this technology in factory automation, particularly in tasks that are repetitive and hazardous. The evolution of mobile robotics is discussed, highlighting two popular solutions in the industry. Automated Guided Vehicle (AGV), initially guided by wires and later by reflective tapes, have evolved into highly automated systems and requires a training step prior to its use. As opposed, Autonomous Mobile Robots (AMR), are fully autonomous, embedded with recent developments on Simultaneous Localization and Mapping (SLAM) and Navigation algorithms to successfully map and plan a trajectory in an unknown environment in real-time. Using the Robot Operational System (ROS) as an open-source modeling and simulation tool, this article also addresses topics related to the implementation of such technology, highlighting challenges to overcome such as uncertainty management, positioning estimation drift mitigation and extraction of environmental features to better estimate the robot positioning in a global environment.
robotics, autonomous navigation, robotics simulation, control modeling, SLAM
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