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Subject: search.filters.subject.autonomous mobile robot

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    AI-Based Optimization Framework for Scheduling Autonomous Rail-Guided Vehicles in Warehouse Systems
    (2026-01-01)
    Keawchai, Rattanaphapra
    ;
    Yanyong, Sarucha
    Scheduling tasks for autonomous Rail-Guided Vehicle (RGV) systems presents a complex optimization challenge that critically influences warehouse automation performance. This research develops an AI-based RGV scheduling framework that allows configuration of robot parameters such as maximum velocity, acceleration, deceleration, and track dimensions, accounting for velocity constraints imposed by curved tracks. The study includes five computational intelligence algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), hybrid GAPSO, and hybrid SUPER-SAPSO. The framework integrates path planning, layered collision penalty models, and multi-RGV task assignment under a physics-based travel time model while minimizing RGV idle time and addressing workload imbalance. Experimental results demonstrate comparative analyses of the efficiency and convergence speed of the various computational intelligence algorithms in optimizing overall warehouse efficiency.
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    Localization for Outdoor Mobile Robot Using LiDAR and RTK-GNSS/INS
    (2024-01-01)
    Thepsit, Thitipong
    ;
    Konghuayrob, Poom
    ;
    Saenthon, Anakkapon
    ;
    Yanyong, Sarucha
    Two types of sensors, light detection and ranging (LiDAR) and real-time kinematic of global navigation satellite system with inertial navigation system (RTK-GNSS/INS), are used for the localization of outdoor mobile robots. However, using LiDAR and RTK-GNSS/INS independently was found to be insufficient for achieving precise positioning. Therefore, a sensor fusion approach based on an adaptive-network-based fuzzy inference system (ANFIS) was implemented to enhance reliability. In this research, data from both sensors were collected to create a dataset for training with ANFIS. The findings indicated that the model derived from the fusion of these two sensors provided results that were much closer to the actual values obtained using each sensor independently. The result demonstrated the effectiveness of the ANFIS-based fusion method in terms of improving the accuracy and reliability of the positioning system for outdoor mobile robots.
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    Energy Prediction of Cleanroom-type Differential Drive Mobile Robot Based on Recurrent Neural Network
    (2023-01-01)
    Yanyong, Sarucha
    ;
    Konghuayrob, Poom
    ;
    Chaisiri, Punyavee
    ;
    Kaitwanidvilai, Somyot
    The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system.