Mobile Robot Navigation Using an Object Recognition Software with RGBD Images and the YOLO Algorithm

Reis, Douglas Henke dos; Welfer, Daniel; Cuadros, Marco Antônio de Souza Leite; Gamarra, Daniel Fernando Tello

doi:10.1080/08839514.2019.1684778

Cited by 46 publications

(7 citation statements)

References 6 publications

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“…1. Problem domain: The design and development of an efficient robot semantic navigation system depend on the specific problem domain [86]. The choice of perception units, robot design, processing unit, and navigation algorithm varies depending on the robot navigation application.…”

Section: Indoor Semantic Navigation Lifecyclementioning

confidence: 99%

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Alqobali,

Alshmrani,

Alnasser

et al. 2023

Applied Sciences

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Robot autonomous navigation has become a vital area in the industrial development of minimizing labor-intensive tasks. Most of the recently developed robot navigation systems are based on perceiving geometrical features of the environment, utilizing sensory devices such as laser scanners, range-finders, and microwave radars to construct an environment map. However, in robot navigation, scene understanding has become essential for comprehending the area of interest and achieving improved navigation results. The semantic model of the indoor environment provides the robot with a representation that is closer to human perception, thereby enhancing the navigation task and human–robot interaction. However, semantic navigation systems require the utilization of multiple components, including geometry-based and vision-based systems. This paper presents a comprehensive review and critical analysis of recently developed robot semantic navigation systems in the context of their applications for semantic robot navigation in indoor environments. Additionally, we propose a set of evaluation metrics that can be considered to assess the efficiency of any robot semantic navigation system.

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Section: Indoor Semantic Navigation Lifecyclementioning

confidence: 99%

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Alqobali,

Alshmrani,

Alnasser

et al. 2023

Applied Sciences

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“…Consequently, this platform could recognize large types of big or tiny objects, ranging in number from 1 to 10. Additionally, YOLOv3 is fast and enables short inference time with high FPS (Frame Per Second) on GPU (Graphical Processing Unit) edge devices [80]. Hence, image classification network developed further developed as compared to regular deep stacks of layers of the earlier forms of YOLO.…”

Section: A Yolov3mentioning

confidence: 99%

Vision-Based Soft Mobile Robot Inspired by Silkworm Body and Movement Behavior

Abed,

Al-Ibadi,

Abed

2023

Journal of Robotics and Control

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Designing an inexpensive, low-noise, safe for individual, mobile robot with an efficient vision system represents a challenge. This paper proposes a soft mobile robot inspired by the silkworm body structure and moving behavior. Two identical pneumatic artificial muscles (PAM) have been used to design the body of the robot by sewing the PAMs longitudinally. The proposed robot moves forward, left, and right in steps depending on the relative contraction ratio of the actuators. The connection between the two artificial muscles gives the steering performance at different air pressures of each PAM. A camera (eye) integrated into the proposed soft robot helps it to control its motion and direction. The silkworm soft robot detects a specific object and tracks it continuously. The proposed vision system is used to help with automatic tracking based on deep learning platforms with real-time live IR camera. The object detection platform, named, YOLOv3 is used effectively to solve the challenge of detecting high-speed tiny objects like Tennis balls. The model is trained with a dataset consisting of images of Tennis balls. The work is simulated with Google Colab and then tested in real-time on an embedded device mated with a fast GPU called Jetson Nano development kit. The presented object follower robot is cheap, fast-tracking, and friendly to the environment. The system reaches a 99% accuracy rate during training and testing. Validation results are obtained and recorded to prove the effectiveness of this novel silkworm soft robot. The research contribution is designing and implementing a soft mobile robot with an effective vision system.

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“…The trajectory tracking control system of the Delta parallel robot uses three identical fuzzy logic controllers to control each of the robot's three servo motors. The demanded trajectory of the TCP is converted into the demanded angle of the drive joint by the kinematic inverse solution calculation and input to the demand trajectory of the TCP is converted into the demand angle of the drive joint by the kinematic inverse solution and input to the controller [ 20 ]. The input to the controller is the difference between the demand angle of the drive joint and the actual angle and its derivative.…”

Section: Fuzzy Neural Network Motion System For Robot With Artificial...mentioning

confidence: 99%

Research on Robot Fuzzy Neural Network Motion System Based on Artificial Intelligence

Hu¹

2022

Computational Intelligence and Neuroscience

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An intelligent controller based on a self-learning interval type-II fuzzy neural network is proposed to make the motion controller of the industrial intelligent robot with good adaptability. This controller has a parallel structure and contains an interval type-II fuzzy neural network and a conventional PD controller. For the design of the interval type-II fuzzy neural network, the interval type-II fuzzy set is established using the slave design method. In the design process of the interval type-II fuzzy set of the front piece, a dual sequence symmetric trapezoidal subordinate function arrangement method is proposed, which makes the self-learning law and stability analysis of the system in an analytic form and facilitates the implementation of the algorithm in hardware. In the design of the neural network self-learning law, a parametric self-learning algorithm based on sliding mode control theory is established to adjust the structural parameters of the interval type-II fuzzy neural network online, and the stability of the system is proved by using Lyapunov’s stability theorem. Three sets of validation simulation experiments are given in conjunction with the trajectory tracking problem of the Delta parallel robot. The simulation results show that, in the presence of system uncertainty, the intelligent controller based on interval self-learning interval type-II fuzzy neural network can significantly improve the trajectory tracking accuracy and robustness of the system and make the control system highly adaptable to the environment. Experiments of intelligent control system based on self-learning interval type-II fuzzy neural network and experiments of reusable particle swarm optimal motion planning method are designed, and the effectiveness of the intelligent control system and motion planning method is verified on the experimental platform. The experimental results show that the intelligent control system based on the self-learning interval type-II fuzzy neural network can effectively improve the accuracy and stability of robot trajectory tracking control, and the reusable particle swarm optimal motion planning method can quickly solve the robot motion planning problem with complex constraints online.

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Mobile Robot Navigation Using an Object Recognition Software with RGBD Images and the YOLO Algorithm

Cited by 46 publications

References 6 publications

A Survey on Robot Semantic Navigation Systems for Indoor Environments

A Survey on Robot Semantic Navigation Systems for Indoor Environments

Vision-Based Soft Mobile Robot Inspired by Silkworm Body and Movement Behavior

Research on Robot Fuzzy Neural Network Motion System Based on Artificial Intelligence

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