Vision-based line tracking and navigation in structured environments

Beccari, G.; Caselli, Stefano; Zanichelli, Francesco; Calafiore, A.

doi:10.1109/cira.1997.613888

Cited by 22 publications

(8 citation statements)

References 7 publications

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“…Line tracking via an onboard camera is perhaps the prospective vision navigation approach mostly used in industrial environments, since it offers real-time, precise and reliable performance. The computational load relevant to this kind of vision perception is mitigated by coding or learning the specific perceptual knowledge that is expected to extract from the environment [15,[19][20][21]. Moreover, gluing colored stripes on the floor is inexpensive, compared not only with laying down a wire under the ground, but also with setting up a plurality of retro reflectors for a laser guidance system [22].…”

Section: Introductionmentioning

confidence: 99%

Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

Ángeles

Zou

et al. 2020

Applied Sciences

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Applying computer vision to mobile robot navigation has been studied for over two decades. One of the most challenging problems for a vision-based mobile robot involves accurately and stably tracking a guide path in the robot limited field of view under high-speed manoeuvres. Pure pursuit controllers are a prevalent class of path tracking algorithms for mobile robots, while their performance is rather limited to relatively low speeds. In order to cope with the demands of high-speed manoeuvres, a multi-loop receding-horizon control framework, including path tracking, robot control, and drive control, is proposed in this paper. This is done within the vision guidance of differential-driving wheeled mobile robots (DWMRs). Lamé curves are used to synthesize a trajectory with G 2 -continuity in the field of view of the mobile robot for path tracking, from its current posture towards the guide path. The platform twist—point velocity and angular velocity—is calculated according to the curvature of the Lamé-curve trajectory, then transformed into actuated joint rates by means of the inverse-kinematics model; finally, the motor torques needed by the driving wheels are obtained based on the inverse-dynamics model. The whole multi-loop control process, initiated from Lamé-curve blending to computational torque control, is conducted iteratively by means of receding-horizon guidance to robustly drive the mobile robot manoeuvring close to the guide path. The results of numerical simulation show the effectiveness of our approach.

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Section: Introductionmentioning

confidence: 99%

Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

Ángeles

Zou

et al. 2020

Applied Sciences

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“…Sci. 2019, 9, 4108 2 of 23 navigation has been studied for more than two decades [4][5][6]. Although the well-known Kiva robot can visually recognize an array of bars or two-dimensional codes on the floor as landmarks [7], cameras are still not widely used for the vision guidance of commercial off-the-shelf (COTS) AGV products in the current industrial applications [8].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

Sun

Zou

et al. 2019

Applied Sciences

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Applying computer vision to mobile robot navigation has been studied more than twodecades. The most challenging problems for a vision-based AGV running in a complex workspaceinvolve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably resultin incomplete or deformed path images as well as many fake artifacts. Neither the fixed thresholdmethods nor the iterative optimal threshold methods can obtain a suitable threshold for the pathimages acquired on all conditions. It is still an open question to estimate the model parameters ofguide paths accurately by distinguishing the actual path pixels from the under- or oversegmentationerror points. Hence, an intelligent path recognition approach based on KPCA–BPNNand IPSO–BTGWP is proposed here, in order to resist the interferences from the complexworkspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of apath classifier based on KPCA–BPNN. Secondly, an approximation method based on BTGWP wasdeveloped for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robustpath estimation method based on IPSO was proposed to figure out the path parameters from a set ofpath pixels surrounded by noise points. Experimental results showed that our approach caneffectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in acomplex workspace.

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“…AGV uses the magnetic sensor to detect the magnetic tape and then follow the path [5], or uses the vision sensor (e.g. camera) to identify the colored tape [6][7]. In order to measure the position of AGV on the path, magnetic and visual coded signs can be used as artificial landmarks to express its absolute coordinates.…”

Section: Introductionmentioning

confidence: 99%

Design and control of material transport system for automated guided vehicle

Lou

Cai

et al. 2012

Proceedings of 2012 UKACC International Conference on Control

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Automated guided vehicle (AGV) is used widely in many industrial applications to transport materials. A long-travel material transport system is designed and controlled in this paper for a unit load AGV to transfer the material pallet from AGV to the load stand and back, including a both-side three-level push-pull load-transfer mechanism and a programmable logic controller (PLC). AGV follows the path of the magnetic tapes in the floor, and locates itself according to the RFID tags beside the load stand. The PLC detects the docking position of AGV and the load stand, and controls the horizontal and vertical movement of the load-transfer mechanism by using a stepper motor and an electric push rod. The load transfer experiments of our AGV testify the performance of this material transport system.

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Vision-based line tracking and navigation in structured environments

Cited by 22 publications

References 7 publications

Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

Receding-Horizon Vision Guidance with Smooth Trajectory Blending in the Field of View of Mobile Robots

Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

Design and control of material transport system for automated guided vehicle

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