High sampling rate IMU with dual band GNSS receiver

Miletiev, Rosen; Kapanakov, Petar; Iontchev, Emil; Yordanov, Rumen

doi:10.1109/isse49702.2020.9121131

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…Apart from road infrastructure analyses, MEMS-based solutions are applied to research the mutual influence on both roads and vehicles as in [ 13 , 14 ]. The authors of [ 13 ] measured vehicle-road interaction with a high sampling rate and better position accuracy, whereas the authors of [ 14 ] developed MEMS-based system in order to assess the condition of the pavement, the condition of the vehicle, and the passengers’ comfort.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The above-described brief literature review can be summarized by indication of general topics considered in the research connected to MEMS sensors used in road transport. These topics are as follows: Measurements of actual roadways’ state [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 12 , 14 ]; Mutual influence analyses on both roads and vehicles [ 13 , 14 ]; Traffic safety, support, and distribution [ 15 , 16 , 17 , 18 ]; Automatic early warning systems in the case of accident incidence on a road system [ 7 , 17 , 19 , 21 , 22 , 23 ]; Measurements of actual vehicle state [ 9 , 10 , 11 , 12 , 13 , 18 , 25 , 29 , 30 , 31 , 34 , 35 , 42 , 43 , 53 , 54 , 55 , 61 , 62 , 63 , 64 ]; Correction of positioning errors and other measuring inadequacy in road transport MEMS-based applications [ 11 …”

Section: Literature Reviewmentioning

confidence: 99%

“…Measurements of actual vehicle state [ 9 , 10 , 11 , 12 , 13 , 18 , 25 , 29 , 30 , 31 , 34 , 35 , 42 , 43 , 53 , 54 , 55 , 61 , 62 , 63 , 64 ];…”

Section: Literature Reviewunclassified

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Application of MEMS Sensors for Evaluation of the Dynamics for Cargo Securing on Road Vehicles

Gnap

Jagelčák

Marienka

et al. 2021

Sensors

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Safety is one of the key aspects of the successful transport of cargo. In the case of road transport, the dynamics of a vehicle during normal events such as braking, steering, and evasive maneuver are variable in different places in the vehicle. Several manufacturers provide different dataloggers with acceleration sensors, but the results are not comparable due to different sensor parameters, measurement ranges, sampling frequencies, data filtration, and evaluation of different periods of acceleration. The position of the sensor in the loading area is also important. The accelerations are not the same at all points in the vehicle. The article deals with the measurement of these dynamic events with MEMS sensors on selected points of a vehicle loaded with cargo and with changes in dynamics after certain events that could occur during regular road transport of cargo to analyze the possibilities for monitoring accelerations and the related forces acting on the cargo during transport. The article uses evaluation times of 80, 300, and 1000 ms for accelerations. With the measured values, it is possible to determine the places with a higher risk of cargo damage and not only to adjust the packaging and securing of the cargo, but also to modify the transport routes. Concerning the purposes of securing the cargo in relation to EN 12195-1 and the minimum values of forces for securing the cargo, we focused primarily on the places where the acceleration of 0.5 g was exceeded when analyzing the monitored route. There were 32 of these points in total, all of which were measured by a sensor located at the rear of the semi-trailer. In 31 cases, the limit of 0.5 g was exceeded for an 80-ms evaluation time, and in one case, the value of 0.51 g was reached in the transverse direction for a 300-ms evaluation time.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Application of MEMS Sensors for Evaluation of the Dynamics for Cargo Securing on Road Vehicles

Gnap

Jagelčák

Marienka

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…To develop the proposed tasks, it is essential to have a good localisation. Conventional methods use GPS [19,20], inertial sensors [21][22][23][24], cameras [25][26][27][28], odometry [29,30], lidars [31][32][33][34][35], kinect [36,37], or combined systems [36,38,39]. Other specific sensors highly used in agriculture are multi-spectral ones, although their application focuses more on the recognition and vegetative analysis than on the location within an environment [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

et al. 2020

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The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants’ geometric characteristics were extracted from the G-PC as a framework in which the robot’s positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%.

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