Restaurant Serving Robot with Double Line Sensors Following Approach

Thanh, Vo Nhu; Dang, Phuoc Vinh; Ngo, Thanh-Nghi; Le, Hoai Nam; Toan, Do Le Hung

doi:10.1109/icma.2019.8816404

Cited by 25 publications

(9 citation statements)

References 1 publication

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“…An example is the multiple brands of vacuum cleaning robots that we can find, such as the emblematic Roomba to newer and less expensive models. Robots in hotels and restaurants start to appear as butlers and bringing trays [76,163]. For example, some hospitals have implemented the Savioke's Relay robot to help in transport tasks such as bringing medicines and food [22,125].…”

Section: Motivationmentioning

confidence: 99%

Adding Uncertainty to an Object Detection System for Mobile Robots

Hernandez

Gómez

Crespo

et al. 2017

2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT)

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I would like to dedicate these lines to express my gratitude to all the people who have helped me in a way or another to achieve this goal that is so important to me. First, I would like to thank my tutor and director, Dr. Ramón Barber, for introducing me to the exciting world of research, for all his support, guidance, trust, and patience over these years of my Ph.D. studies. Thank you for giving me the opportunity to work with you. I also thank Dr. Oscar Martínez, for all the advice and technical talks and for allowing me to visit his laboratory in Sweden. I would like to thank the University Carlos III of Madrid and all the Robotics Lab members I met during these years. Thank you for your support, professionalism, and friendship; It is an honor to belong to this great research group.I would like to especially thank my lab partner Clara Gómez. Thank you for being such a great partner and friend over these years, for your support, professionalism, your dedication at work. Thank you for being my partner in conferences and research visits. Definitely, the Ph.D. would not have been the same without you.I would like to thank Dr. Zoltan Marton and his team for giving me the opportunity to work with them at the DLR institute in Germany. I would also like to thank Prof. Robert Babuška for welcoming me to his lab at the CTU in Prague. Especially thanks to Erik Derner for being a great lab partner, for our interesting discussions, for his professionalism and friendship. I also thank the people I met at Örebro University, especially Eduardo Gutiérrez; thank you for your support and friendship.Finally and most importantly, I would like to thank my family. Thank you for all your support, your love, and for always being there. Despite the more than 7000 km. that separate us, I am always with you, and I know that you are with me celebrating this achievement. I leave for last, the person I must thank for having embarked on this path. I want to thank my husband, Jesús. Thank you for supporting me and helping me accomplish this goal, for being my partner on this roller coaster of life, for always believing in me, and for having a word of encouragement when I felt like I could not continue. Without you, I am sure I would not have made it. For you all my love and my gratitude.To all my sincere thanks.

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Section: Motivationmentioning

confidence: 99%

Adding Uncertainty to an Object Detection System for Mobile Robots

Hernandez

Gómez

Crespo

et al. 2017

2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT)

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“…They provided a comparison of the existing shortcomings over the system they developed and provided a reference for future studies. [18].…”

Section: Introductionmentioning

confidence: 99%

Development of Image Processing Based Line Tracking Systems for Automated Guided Vehicles with ANFIS and Fuzzy Logic

YÜKSEK,

ELİK

2023

Cumhuriyet Science Journal

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Automated Guided Vehicles (AGVs) are robotic vehicles with the ability to move using mapping and navigation technologies to perform tasks assigned to them, guided by guides. Using sensor data such as laser scanners, cameras, magnetic stripes or colored stripes, they can sense their environment and move safely according to defined routes. The basic requirement of motion planning is to follow the path and route with minimum error even under different environmental factors. The key factor here is the most successful detection of the guiding structure of a system moving on its route. The proposed system is to equip a mechanical system that can produce very fast outputs and autonomous motion as a result of combining different algorithms with different hardware structures. In the line detection process, the wide perspective image from the camera is designed to be gradually reduced and converted into image information that is more concise but representative of the problem in a narrower perspective. In this way, the desired data can be extracted with faster processing over less information. In this study, the image information is divided into two parts and planned as two different sensors. The fact that the line information was taken from two different regions of the image at a certain distance enabled the detection of not only the presence of the line but also the flow direction. With the fuzzy system, the performance of the system was increased by generating PWM values on two different hardware structures, loading image capture, image processing processes and driving the motors. In order to determine the membership function parameters of the fuzzy system for each input, the ANFIS approach was used on the data set modeling the system. The outputs produced by the ANFIS model were combined into a single fuzzy system with two outputs from the system rules framework and the system was completed. The success of the algorithms was ensured by partitioning the task distribution in the hardware structure. With its structure and success in adapting different technologies together, a system that can be recommended for similar problems has been developed.

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“…In general, mobile robots can replace humans in dangerous working environments, such as fires [7] and military environments. In addition, these robots are often tasked with goods deliveries in indoor areas with repetitive movements, such as hospitals [8] and restaurants [9]. Notably, autonomous driving of mobile robots has endowed them with the ability to perform all such activities.…”

Section: Introductionmentioning

confidence: 99%

Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments

Park

Lee

Hong

et al. 2022

Sensors

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In this paper, we propose a deep deterministic policy gradient (DDPG)-based path-planning method for mobile robots by applying the hindsight experience replay (HER) technique to overcome the performance degradation resulting from sparse reward problems occurring in autonomous driving mobile robots. The mobile robot in our analysis was a robot operating system-based TurtleBot3, and the experimental environment was a virtual simulation based on Gazebo. A fully connected neural network was used as the DDPG network based on the actor–critic architecture. Noise was added to the actor network. The robot recognized an unknown environment by measuring distances using a laser sensor and determined the optimized policy to reach its destination. The HER technique improved the learning performance by generating three new episodes with normal experience from a failed episode. The proposed method demonstrated that the HER technique could help mitigate the sparse reward problem; this was further corroborated by the successful autonomous driving results obtained after applying the proposed method to two reward systems, as well as actual experimental results.

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Restaurant Serving Robot with Double Line Sensors Following Approach

Cited by 25 publications

References 1 publication

Adding Uncertainty to an Object Detection System for Mobile Robots

Adding Uncertainty to an Object Detection System for Mobile Robots

Development of Image Processing Based Line Tracking Systems for Automated Guided Vehicles with ANFIS and Fuzzy Logic

Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments

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