Effective anti-collision algorithms for RFID robots system

With the rapid development of sensor technology for automated driving applications, the fusion, analysis, and application of multimodal data have become the main focus of different scenarios, especially in the development of mobile edge computing technology that provides more efficient algorithms for realizing the various application scenarios. In the present paper, the vehicle status and operation data were acquired by vehicle-borne and roadside units of electronic registration identification of motor vehicles. In addition, a motion model and an identification system for the single-vehicle lane-change process were established by mobile edge computing and self-organizing feature mapping. Two scenarios were modeled and tested: lane change with no vehicles in the target lane and lane change with vehicles in the target lane. It was found that the proposed method successfully identified the stochastic parameters in the process of driving trajectory simulation, and the standard deviation between simulation and the measured results obeyed a normal distribution. The proposed methods can provide significant practical information for improving the data processing efficiency in automated driving applications, for solving single-vehicle lane-change applications, and for promoting the formation of a closed loop from sensing to service.

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“…Let the following equations describe an n-dimensional linear dynamic system and an m-dimensional linear observation system [26][27][28]:…”

Section: Stochastic Parameter Identification Methods Based On Self-mentioning

confidence: 99%

Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self‐Organizing Feature Mapping

et al. 2021

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“…e tag helps the reader to judge whether the tags in the identification area collide with each other or not by sending a PBD burst time. If collision occurs, the collision problem will be solved by recursive polling, thereby improving the tag identification rate of the system [17]. In August 2016, Impinj proposed a scheme of the Speedway Revolution RFID reader, to which automatic performance setting is introduced based on environmental noise detection and on automatic dynamic antenna switching technology to optimize read-write time and efficiency [18].…”

Section: Related Workmentioning

confidence: 99%

“…where M is the number of input variables which is 6 in this study. Hence, according to equations ( 16) and (17), the Mtry value is 2. When the nodes are divided in the subset, the Gini index in CART algorithm is taken as the minimum principle to select the optimal split influencing factor and optimal split value.…”

Section: Respectively (2) Bootstrap Sampling Training Subsets and Decisionmentioning

confidence: 99%

Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios

Wang

Pan

et al. 2020

Complexity

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With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields. In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge. In this paper, a neighborhood rough set and random forest (NRS-RF) combination model is proposed to predict the identification rate of an RFID system. Firstly, the initial influencing factors of the RFID system identification rate are reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset. Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag (OOB) data to evaluate the prediction results. The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%. It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance. Compared with BP neural network (BPNN) and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform.

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“…More than 90% of environmental information is obtained through machine vision (Zhang et al, 2011;Li et al, 2009). In terms of lane recognition, several systems, including the ALVINN system, SCARE system, RALPH system, GOLD system and LOTS system, have been put into operation (Wang et al, 2019;Yang et al, 2020;Shin et al, 2014;Gu et al, 2014;Du and Tan, 2016;Shen et al, 2015;Hans and Ghosh, 2020;Zeng et al, 2018). Generally, the model-based method is currently the most common and most effective lane recognition method.…”

Section: Introductionmentioning

confidence: 99%

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Song

Zhou

Chang³

et al. 2021

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Purpose Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc. Design/methodology/approach Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed. Findings The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments. Originality/value In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

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Effective anti-collision algorithms for RFID robots system

Cited by 11 publications

References 33 publications

Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self‐Organizing Feature Mapping

Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self‐Organizing Feature Mapping

Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

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