Neuromorphic controller for AGV steering

Cheng, R. M. H.; Xiao, J.; LeQuoc, S.

doi:10.1109/robot.1992.219978

Cited by 16 publications

(6 citation statements)

References 13 publications

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“…Cheng et al used a Back-Propagation neural network as a controller for an automated vehicle system. Camera images were used as inputs to the neural network [11]. Tomar et al proposed a method to give the future lane-changing trajectories accurately for discrete patches using a multilayer perceptron (MLP) [12].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Ding

Wang

et al. 2013

Mathematical Problems in Engineering

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The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.

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Section: Introductionmentioning

confidence: 99%

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Ding

Wang

et al. 2013

Mathematical Problems in Engineering

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“…As mentioned earlier, an AGV was introduced in 1953, and in the following years, AGVs were integrated into warehousing and logistic activities using track guided magnetic systems, optical sensors and color strips as AGV guidance technologies [1]. Advancing technologies brought about the use of transistors, vacuum tubes, microprocessors, microcomputers [38], infrared, radio signal guidance and programable logic controllers (PLC) [1].…”

Section: Review Of Agv Research Work In the Past Decadementioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.INDEX TERMS Intelligent factory, factory of the future, 5G, smart manufacturing, industry 4.0, autonomous industrial equipment, AGV, AMR, tactile Internet, virtual reality, lean manufacturing.

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“…Most of the systems developed worldwide are based on lane detection: first, the relative position of the vehicle with respect to the lane is computed, and then actuators are driven to keep the vehicle in a safe position. Others [15], [28], [38] are not based on the preliminary detection of the road position, but, as in the case of ALVINN [43], [44], derive the commands to issue to the actuators (steering wheel angles) directly from visual patterns detected in the incoming images. In any case, the knowledge of the lane position can be of use for other purposes, such as the determination of the regions of interest for other driving assistance functions.…”

Section: A Lane Detectionmentioning

confidence: 99%

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

Bertozzi

Broggi

1998

IEEE Trans. on Image Process.

892

404

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Abstract-This paper describes the Generic Obstacle and Lane Detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an onboard monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.

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Neuromorphic controller for AGV steering

Cited by 16 publications

References 13 publications

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

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