Motion Route Planning and Obstacle Avoidance Method for Mobile Robot Based on Deep Learning

Ji-chao, Cui; Nie, Guanghua

doi:10.1155/2022/5739765

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The diagram indicates the proximity between the keywords based on their frequency in the database. The keyword "reinforcement learning" had a total of 82 occurrences and is closely related to "obstacle avoidance", meaning that RL methodology is constantly applied to that specific task; the D3QN Network [28], the simulator proposed in [33]; and the motion strategy from [2].…”

Section: Investigation Tendenciesmentioning

confidence: 99%

“…The current endeavor in mobile robotics is mainly focused on applying artificial intelligence to different levels of the whole robotic system [1]. This includes velocity control [2], instrumentation, sensor fusion [3], object recognition, etc. However, path planning is a particular task that broadens the reliability of any robot, simplifies the interaction with a human operator, and, ultimately, is common to all types of autonomous robots [4], not only mobile robots.…”

Section: Introductionmentioning

confidence: 99%

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Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms

Galarza-Falfan,

García-Guerrero,

Aguirre-Castro

et al. 2024

Technologies

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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.

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Section: Investigation Tendenciesmentioning

confidence: 99%