Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms

• Published in: Technologies (Basel) Vol. 12; no. 6; p. 82
• Main Authors: Galarza-Falfan, Jorge, Garcia-Guerrero, Enrique Efren, Aguirre-Castro, Oscar Adrian, Lopez-Bonilla, Oscar Roberto, Tamayo-Perez, Ulises Jesus, Cardenas-Valdez, Jose Ricardo, Hernandez-Mejia, Carlos, Borrego-Dominguez, Susana, Inzunza-Gonzalez, Everardo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Artificial vision; autonomous mobile robot; Collaboration; Comparative analysis; Control; Deep learning; Effectiveness; Embedded systems; Energy consumption; Location-based systems; Machine learning; Machine vision; Mobile robots; navigation; Neural networks; Object recognition; Obstacle avoidance; Open source software; Path planning; Pattern recognition; Performance evaluation; Performance measurement; Planning; Real time; Recall; reinforcement learning; Robot dynamics; Robot learning; Robotics; Robots; Sensors; Simultaneous localization and mapping; Technology application; Unmanned aerial vehicles; Vision systems; Mexico
• DOI: 10.3390/technologies12060082
• ISSN: 2227-7080

Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.