Research of Autonomous Navigation Strategy for an Outdoor Mobile Robot

Zhou, Miaolei; He, Shanbo

doi:10.14257/ijca.2014.7.12.32

Cited by 3 publications

(1 citation statement)

References 14 publications

(16 reference statements)

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“…On the other hand, accuracy of the digital compass can be easily affected by magnetic interference. Particularly if the mobile robot passes through stray magnetic fields as part of its course of navigation, large errors will occur in the heading angle of the mobile robot (Zhou and He, 2014).…”

Section: Introductionmentioning

confidence: 99%

Development of angular correction algorithm for movement of agricultural mobile robots in a straight line

Unal,

Kabas,

Eceoglu

2023

CEAI

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This paper proposes the angular correction algorithm for the autonomous navigation of the agricultural mobile robots, which are driven in a straight line, with simple hardware based on the data of the digital compass and the GPS receiver. The motion of the mobile robot was accomplished by the differential drive mechanism with four driving wheels in which the overall velocity is split between left and right wheels. The two-channel DC motor controller was used to drive motors. The digital compass was used to calculate the position angle of the mobile robot with respect to the target point. A Kalman filter was used to fuse the information from GPS and digital compass. In the proposed algorithm, the mobile robot is driven in a straight line along a predefined path by calculating in real time the deviation angle difference with respect to the target point. When the robot encounters an unexpected external force varying the desired path, it achieves a smooth and stabilized straight line movement by correcting the deviation angle difference. The performance of the mobile robot was evaluated based on a total of 18 straight lines in a farmland. Standard errors of cross track error (XTE) values of straight lines for each target point were analyzed. The mean of arithmetic means was found to be 4.14 cm. The mean of Rsquare was 0.990. This value shows that the proposed angular correction algorithm is useful in driving the mobile robot in a straight line.

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Section: Introductionmentioning

confidence: 99%

Development of angular correction algorithm for movement of agricultural mobile robots in a straight line

Unal,

Kabas,

Eceoglu

2023

CEAI

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Mobile robot for power substation inspection: a survey

Zhang

2017

IEEE/CAA J. Autom. Sinica

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Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes

et al. 2020

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Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its importance and usage in power plants and power systems. In this paper, three different applications using this approach are described: (i) optimal dam management in hydroelectric power plants, (ii) inspection and surveillance in electric substations, and (iii) optimization of steam generation in a combined cycle power plant. For each case, the technique has demonstrated to find optimal action policies in uncertain settings, present good response and compilation times, deal with stochastic variables and be a good alternative to traditional control systems. The main contributions of this work are as follows, a methodology to approximate a decision model using machine learning techniques, and examples of how to specify and solve problems in the electric power domain in terms of a FMDP.

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Research of Autonomous Navigation Strategy for an Outdoor Mobile Robot

Abstract: Abstract

Cited by 3 publications

References 14 publications

Development of angular correction algorithm for movement of agricultural mobile robots in a straight line

Development of angular correction algorithm for movement of agricultural mobile robots in a straight line

Mobile robot for power substation inspection: a survey

Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes

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