Research on autonomous navigation of mobile robot based on multi ultrasonic sensor fusion

Wang, Rui; Chen, Lei; Wang, Jie; Zhang, Pei; Tan, Qimeng; Dong, Peng

doi:10.1109/itoec.2018.8740655

Cited by 13 publications

(6 citation statements)

References 2 publications

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“…Similar to radars, ultrasonic sensors calculate the object-source distance by measuring the time between the transmission of an ultrasonic signal and its reception by the receiver. Ultrasonic sensors are commonly used to autonomously locate and navigate a vehicle [61]. GPS and IMU are additional widely used sensors for autonomous navigation systems.…”

Section: Basic Sensorsmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation.

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Section: Basic Sensorsmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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“…The receiver utilizes the texts it gets to determine the travel time of each message and to calculate the receiver distance from each satellite. Ultrasonic distance sensor [22]- [24]: The ultrasonic range modules are used to calculate obstacle distance from the car. These sensors have a maximum range of 400cm and maximum effectual angle of 150 [25]- [27] Each sensor is connected to AVR and power supply via a 4 pin wire connector.…”

Section: A System Descriptionmentioning

confidence: 99%

Development of a Prototype Autonomous Electric Vehicle

Mohammed

Abdullahi

Ibrahim

2021

jrc

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The paper presents an Autonomous Electric Vehicle with obstacle avoidance system. This research work made use of ultrasonic sensors, the principle of distance measurements and calculations as well as detecting obstacle on its path. The device consists of three ultrasonic sensors that detect object for each left, right and front of the vehicle, based on developed and installed codes in the Arduino microcontroller and displays the range using ISIS Proteus 8 electronic modelling software. The minimum and maximum range of object detections is 2cm to 400cm respectively. However, the measured distance was from 25cm to 150cm and the corresponding calculated distances using oscilloscope waveforms are 28.10cm and 148.3cm. The difference between the measured and calculated distance was 5.4% on average. GPS navigates the vehicle autonomously to its destination using an algorithm for navigation based on reactive behavior. The vehicle is powered by rechargeable batteries (4 lithium ion batteries) which are charged using external power source by connecting into electricity grid. Furthermore, a solar panel has been utilized as a secondary source of power to charge the batteries. This reduces the dependency of the vehicle on external power sources. The vehicle is capable of moving for about 20m to and fro and avoiding obstacle on its path.

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“…Jongil Lim et al [11] made rotating ultrasonic to make the environment reading faster and decrease the ultrasonic needed in one robot. Rui Wang et al [12] developed multi-sensor ultrasonic to become more accurate. To optimize the environment reading, Denysyuk et al [13], Y. Phang, W. et al [14], M. Takahashi et al [16], and S. Premnath et al [17] used LIDAR.…”

Section: Introductionmentioning

confidence: 99%

Position correction of mobile robot by detecting line landmark using Hough line transform

Anggoro,

Setiawan,

Nurhayati

2023

J. Phys.: Conf. Ser.

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Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρMAX) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position.

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Research on autonomous navigation of mobile robot based on multi ultrasonic sensor fusion

Cited by 13 publications

References 2 publications

Computer Vision in Self-Steering Tractors

Computer Vision in Self-Steering Tractors

Development of a Prototype Autonomous Electric Vehicle

Position correction of mobile robot by detecting line landmark using Hough line transform

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