Grid-Based Improved Maximum Likelihood Estimation for Dynamic Localization of Mobile Robots

Feng, Sheng; Wu, Chengdong; Zhang, Yunzhou; Jia, Zhenhong

doi:10.1155/2014/271547

Cited by 14 publications

(11 citation statements)

References 13 publications

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“…The estimation results were compared with the actual driving distance in the data. Root-mean-square error (RMSE) and root-mean-square relative error (RMSRE) were used as performance indexes to analyze the accuracy of the model scientifically [35]. RMSE and RMSRE are given in Eqs.…”

Section: Model Verificationmentioning

confidence: 99%

A data-based model for driving distance estimation of battery electric logistics vehicles

Wang

Zhang

2018

J Wireless Com Network

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Battery electric logistics vehicles (BELVs) reduce transportation costs and air pollution unlike conventional logistics vehicles. However, the limited driving range of BELVs creates new problems for logistics transport. Accurate driving distance estimation of BELVs can help logistics companies determine transport strategies and alleviate the range anxiety of drivers. Based on mass data from BELVs operating in Beijing, China, this study uses a practical and effective data-based modeling method, regression analysis, to establish the data-based model of driving distance estimation. During the modeling process, a nonlinear relation between percentage of energy consumption per kilometer and driving speed is explored based on the experimental data. After determining the model variables, the model frame of driving distance in consideration of driving speed and state of charge is established. The forgetting factor recursive least-squares algorithm is applied to estimate the parameter values of the model. Verification results confirm the feasibility of the model and show that the model errors are small. The proposed model is also used to explore the economical driving speed of BELVs.

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Section: Model Verificationmentioning

confidence: 99%

A data-based model for driving distance estimation of battery electric logistics vehicles

Wang

Zhang

2018

J Wireless Com Network

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“…To further verify the error of the model, the mean error (EMean), root mean square error (RMSE) and root mean square relative error (RMSRE) were used as the indexes to test the model [28] on the basis of three sets of charging data not used for modelling. The results are shown in Table 5, where the mean error is less than 0.07, the root mean square error is less than 0.09 and the root mean square relative error is less than 0.016.…”

Section: Basic Regression Modelmentioning

confidence: 99%

Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

Wang

Sun

et al. 2018

Energies

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Battery electric vehicles (BEVs) reduce energy consumption and air pollution as compared with conventional vehicles. However, the limited driving range and potential long charging time of BEVs create new problems. Accurate charging time prediction of BEVs helps drivers determine travel plans and alleviate their range anxiety during trips. This study proposed a combined model for charging time prediction based on regression and time-series methods according to the actual data from BEVs operating in Beijing, China. After data analysis, a regression model was established by considering the charged amount for charging time prediction. Furthermore, a time-series method was adopted to calibrate the regression model, which significantly improved the fitting accuracy of the model. The parameters of the model were determined by using the actual data. Verification results confirmed the accuracy of the model and showed that the model errors were small. The proposed model can accurately depict the charging time characteristics of BEVs in Beijing.

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“…ℎ < . ℎ then (15) reach.remove(point) (16) end (17) select 5 m as the threshold), the output is the point with the maximal value of 1 and 2 . Otherwise, we further check the weight and output the one with the smaller weight.…”

Section: Path Findingmentioning

confidence: 99%

Waypoint Graph Based Fast Pathfinding in Dynamic Environment

Zhu

Jia

Wan

et al. 2015

International Journal of Distributed Sensor Networks

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Pathfinding is a fundamental task in many applications including robotics, computer games, and vehicle navigation. Waypoint graph is often used for pathfinding due to its advantage in specifying the space and obstacles in a region. Currently the waypoint graph based pathfinding suffers from large computation overhead and hence long latency in dynamic environment, where the location of obstacles may change. In this paper, we propose a fast approach for waypoint graph based pathfinding in such scenario. We eliminate unnecessary waypoints and edges to make the graph sparse. And then we design a prediction-based local method to handle the dynamic change in the environment. Extensive simulation has been done and the results show that the proposed approach outperforms existing approaches.

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Grid-Based Improved Maximum Likelihood Estimation for Dynamic Localization of Mobile Robots

Cited by 14 publications

References 13 publications

A data-based model for driving distance estimation of battery electric logistics vehicles

A data-based model for driving distance estimation of battery electric logistics vehicles

Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing

Waypoint Graph Based Fast Pathfinding in Dynamic Environment

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