Sliding mode control of a line following robot

Yıldız, Hüseyin; Can, Nuray Korkmaz; Özgüney, Ömür Can; Yağiz, Nurkan

doi:10.1007/s40430-020-02645-3

Cited by 19 publications

(4 citation statements)

References 27 publications

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“…To find a solution to the obstacle avoidance problem, several methods have been proposed in the literature in recent years [18][19][20][21][22]. There are a variety of sensors that can be used for obstacle detection.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Azurmendi,

Zulueta,

Lopez-Guede

et al. 2023

Electronics

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Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we propose a simultaneous object-detection and distance-estimation algorithm based on YOLOv5 for obstacle detection in indoor autonomous vehicles. This method estimates the distances to the desired obstacles using a single monocular camera that does not require calibration. On the one hand, we train the algorithm with the KITTI dataset, which is an autonomous driving vision dataset that provides labels for object detection and distance prediction. On the other hand, we collect and label 100 images from a custom environment. Then, we apply data augmentation and transfer learning to generate a fast, accurate, and cost-effective model for the custom environment. The results show a performance of mAP0.5:0.95 of more than 75% for object detection and 0.71 m of mean absolute error in distance prediction, which are easily scalable with the labeling of a larger amount of data. Finally, we compare our method with other similar state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Azurmendi,

Zulueta,

Lopez-Guede

et al. 2023

Electronics

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“…The pure pursuit algorithm [11,12] does not consider the vehicle's force conditions, and its tracking performance relies on the selection of the lookahead distance, making it difficult to obtain an optimal solution and resulting in variable tracking effectiveness. Sliding mode control [13,14] is prone to chattering due to delays, which affects vehicle stability and tracking precision when sensor signals fluctuate or actuator control is suboptimal. Model predictive control (MPC) [15,16] increases the computational burden and time consumption with the addition of model dimensions and constraints, making it challenging to tune for practical application.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking and Anti-Roll Control of Unmanned Mining Trucks on Mine Site Roads

Wang,

Wan,

et al. 2024

WEVJ

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Aiming to address the tracking accuracy and anti-rollover problem of the unmanned mining truck path tracking process under the complex unstructured road conditions in mining areas, a coordinated control strategy for path tracking and anti-rollover based on topology theory is proposed. Moreover, optimal equilibrium weights are assigned to path tracking control and anti-rollover control to ensure that the path tracking accuracy of the mining vehicle can be effectively improved in a safe and stable driving state. Regarding the path tracking problem, a lateral preview error model is established, and a path tracking controller is designed using LQR (linear quadratic regulator) control theory. In the design of the anti-rollover controller, the effects of understeer and trip-type rollover on the stability of the vehicle are taken into account, and the ideal transverse swing angular velocity and trip-type rollover evaluation index are introduced for controller design, which reduce the effects of the curves and roadway excitation on the mining truck and improve the rollover motion. Based on a joint simulation using Trucksim and Simulink and the construction of a hardware-in-the-loop simulation platform for verification, the single control strategy and coordinated control strategy are compared and analyzed. The final simulation results show that the tracking error, yaw velocity, and center of mass side deviation angle are optimized by 45%, 32.5%, and 20%, respectively. Therefore, the Extension theory-based coordinated controller satisfies the complex road conditions in the mining area and improves the tracking accuracy to the maximum extent while ensuring the safety and smoothness of vehicle driving and exhibiting good adaptability and robustness.

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“…In some cases, it is not necessary to know the exact map of the environment, for example, in the case of warehousing cargo-bots [7][8][9]. For the warehouse automation, the line follower machines can detect and follow a line drawn on the floor.…”

Section: Introductionmentioning

confidence: 99%

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

Somlyai

Vamossy

2022

Machines

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In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information can also be gathered along with ordinary RGB color data. Using these RGB-D sensors, two- and three-dimensional point clouds can be recorded from the mobile devices, which provide additional information for localization and mapping. The method of matching point clouds during the movement of the device is essential: reducing noise while having an acceptable processing time is crucial for a real-life application. In this paper, we present a novel ISVD-based method for displacement estimation, using key points detected by SURF and ORB feature detectors. The ISVD algorithm is a fitting procedure based on SVD resolution, which removes outliers from the point clouds to be fitted in several steps. The developed method removes these outlying points in several steps, in each iteration examining the relative error of the point pairs and then progressively reducing the maximum error for the next matching step. An advantage over relevant methods is that this method always gives the same result, as no random steps are included.

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Sliding mode control of a line following robot

Cited by 19 publications

References 27 publications

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles

Path Tracking and Anti-Roll Control of Unmanned Mining Trucks on Mine Site Roads

ISVD-Based Advanced Simultaneous Localization and Mapping (SLAM) Algorithm for Mobile Robots

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