Rejection of sliding effects in car like robot control: application to farm vehicle guidance using a single RTK GPS sensor

Lenain, Roland; Thuilot, Benoı̂t; Cariou, C.; Martinet, Philippe

doi:10.1109/iros.2003.1249748

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…In previous work (Lenain et al, 2003b), a complete adaptive control scheme was developed to steer a vehicle in the presence of sliding, relying on a slightly different extended kinematic model. In this paper, it is proposed to take advantage of the special structure of the extended kinematic model (6) in order to design a non-linear control law, providing better reactivity with respect to the phenomenon of liding.…”

Section: Chained System Transformationmentioning

confidence: 99%

High accuracy path tracking for vehicles in presence of sliding: Application to farm vehicle automatic guidance for agricultural tasks

Thuilot²,

et al. 2006

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When designing an accurate automated guidance system for vehicles, a major problem is sliding and pseudosliding effects. This is especially the case in agricultural applications, where five-centimetre accuracy with respect to the desired trajectory is required, although the vehicles are moving on slippery ground.It has been established that RTK GPS was a very suitable sensor to achieve automated guidance with such high precision: several control laws have been designed for vehicles equipped with this sensor, and provide the expected guidance accuracy as long as the vehicles do not slide. In previous work, further control developments have been proposed to take sliding into account: guidance accuracy in slippery environments has been shown to be preserved, except transiently at the beginning/end of curves. In this paper, the design of this control law is first recalled and discussed. A Model Predictive Control method is then applied in order to preserve accuracy of guidance even during these curvature transitions. Finally, the overall control scheme is implemented, and improvements with respect to R. Lenain ( ) · C. Cariou Cemagref, 24 av. des Landais BP50085, 63172 Aubière Cedex,

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Section: Chained System Transformationmentioning

confidence: 99%

High accuracy path tracking for vehicles in presence of sliding: Application to farm vehicle automatic guidance for agricultural tasks

Thuilot²,

et al. 2006

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“…A real-time kinematic (RTK) GPS sensor can help the robot to get an accurate position. It is commonly used in wide-open space [20]. Laser SLAM is now a relatively mature way to do mapping and localization (LiDAR odometry and mapping).…”

Section: Related Workmentioning

confidence: 99%

Autonomous Dam Surveillance Robot System Based on Multi-Sensor Fusion

Zhang

Zhan

Wang

et al. 2020

Sensors

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Dams are important engineering facilities in the water conservancy industry. They have many functions, such as flood control, electric power generation, irrigation, water supply, shipping, etc. Therefore, their long-term safety is crucial to operational stability. Because of the complexity of the dam environment, robots with various kinds of sensors are a good choice to replace humans to perform a surveillance job. In this paper, an autonomous system design is proposed for dam ground surveillance robots, which includes general solution, electromechanical layout, sensors scheme, and navigation method. A strong and agile skid-steered mobile robot body platform is designed and created, which can be controlled accurately based on an MCU and an onboard IMU. A novel low-cost LiDAR is adopted for odometry estimation. To realize more robust localization results, two Kalman filter loops are used with the robot kinematic model to fuse wheel encoder, IMU, LiDAR odometry, and a low-cost GNSS receiver data. Besides, a recognition network based on YOLO v3 is deployed to realize real-time recognition of cracks and people during surveillance. As a system, by connecting the robot, the cloud server and the users with IOT technology, the proposed solution could be more robust and practical.

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“…As a result, the actual robot behavior is quite different from the one predicted by the kinematic models derived from non-sliding assumptions [8]. The tracking control laws have then to be adapted to the changing interaction between robot and environment, otherwise convergence to the reference path may no longer be ensured [14].…”

Section: Introductionmentioning

confidence: 99%