Path Planning of Mobile Robot Based on Improved A* Algorithm

Gao, Ju; Xu, Xiangrong; Zhang, Xingning; Xu, Shidang; Pu, Quancheng

doi:10.1109/wcmeim56910.2022.10021353

Cited by 1 publication

(2 citation statements)

References 18 publications

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“…First of all, the data that has been collected previously, such as the number of motorcycles, the number of cars, the number of lanes, and the distance traveled at a certain time, is used as input for the Random Forest algorithm. This data is used to provide information to the algorithm regarding various factors related to traffic at the time of observation, as shown in (1).…”

Section: B Random Forestmentioning

confidence: 99%

“…In the face of congestion, drivers often look for alternative routes with the shortest distance to travel to waste as little time as possible. However, the lack of information about the state or condition of the road on the route to be passed can also hinder the search for alternative routes that can be used as the best route to get to the destination if the main route has obstacles [1].…”

Section: Introductionmentioning

confidence: 99%

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The intelligent decision model for determine the best path of transportation on smart city using random forest algorithm and bayesian optimization (RF-BO)

Oklilas,

Kamilia,

Abdurahman

et al. 2023

J.INFOTEL

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This study investigates various approaches and algorithms in the context of object detection and best path determination for managing vehicular traffic in an urban environment, particularly in Palembang city. This research is a step towards the development of smart city concept. In the object detection analysis, we applied the YOLOv3 method on video footage to identify vehicles, resulting in mAP accuracy rates between 72.72% to 79.35% for both motorcycle and car categories. The total detection accuracy of the model reached 76.03%. Next, we adopted the Random Forest algorithm to classify traffic conditions into three classes: smooth, moderate, and congested. After optimizing the algorithm with Bayesian Optimization, the model accuracy increased from 89% to 92%, while the classification accuracy increased from 91.66% to 92.36%. Results from the application of the A* Heuristic Search algorithm revealed that lane 5 (from SMK PGRI 1 Palembang to Bom Baru Jl Perintis Kemerdekaan Arah Charitas (STMIK MBC)) was selected most frequently in 9 out of 12time trials. The selection of this route was based on an evaluation of traffic levels that tended to be "smooth" and the shortest travel distance compared to other alternative routes. The decision in choosing the optimal path also considers the road width factor, where wider roads have the potential to reduce traffic density and the risk of congestion.

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Section: B Random Forestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%