scholarly journals The Autonomous Shopping-Guide Robot in Cashier-Less Convenience Stores

2020 ◽  
Vol 14 ◽  
pp. 09-15
Author(s):  
Po-Han Lin ◽  
Cheng-Yi Lin ◽  
Chen-Ting Hung ◽  
Jen-Jee Chen ◽  
Jia-Ming Liang
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Route Planning ◽  
Tag Identification ◽  
Traffic Condition ◽  
Shopping Cart ◽  
Shopping List ◽  
Convenience Stores ◽  
Shopping Experience ◽  
Dynamic Route

In recent years, several cashier-less convenience stores have appeared, including Amazon’s. A store without cashiers will become a trend in the near future. To substitute the employee in traditional stores, this research proposes and designs an autonomous shopping cart robot to guide customers’ purchase according to the requested shopping list to enhance their shopping experience. The core techniques of the system are the autonomous driving robot, the traffic control center for robots and the dynamic route planning algorithm. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving via image recognition, April tag identification and driving direction guidance from the path planning and traffic control services. This enables the robot to lead customers to find their commodities following the preplanned route. In conjunction with the vocal service, the robot can notify the customer when arriving at each commodity, he or she plans to buy. We also design a light APP for customers to easily set up and manage their shopping list, call for the robotic shopping cart’s help, and interact with the shopping cart robot. To enhance the shopping experience of customers, we design the dynamic route planning genetic algorithm to dynamically plan the shopping route according to the customer’s request and the traffic condition. Experiments show that our genetic algorithm can provide the most stable performance and always get efficient shopping route planning in a limited time compared to other methods.

scholarly journals An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

2013 ◽  
Vol 23 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Fei Yan ◽  
Mahjoub Dridi ◽  
Abdellah El Moudni
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Control Methods ◽  
Traffic Condition ◽  
Sequencing Problem ◽  
Genetic Algorithm Approach ◽  
Short Time ◽  
Intersection Management

This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.

Commercial Vehicle Route Planning Design Based on Partition Coding Genetic Algorithm

2020 ◽  
Author(s):  
Kailang Chen ◽  
Hanyu Zhang ◽  
Xin Huang ◽  
Long Chen ◽  
Qi Shi
Keyword(s):  
Genetic Algorithm ◽  
Commercial Vehicle ◽  
Route Planning ◽  
Vehicle Route

scholarly journals Multi-Branch Cable Harness Layout Design Based on Genetic Algorithm with Probabilistic Roadmap Method

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Yingfeng Zhao ◽  
Jianhua Liu ◽  
Jiangtao Ma ◽  
Linlin Wu
Keyword(s):  
Genetic Algorithm ◽  
Topological Structure ◽  
Design Method ◽  
Route Planning ◽  
Layout Design ◽  
Structure Design ◽  
Prototype System ◽  
Cable Harness ◽  
Probabilistic Roadmap ◽  
Probabilistic Roadmap Method

AbstractCurrent studies on cable harness layouts have mainly focused on cable harness route planning. However, the topological structure of a cable harness is also extremely complex, and the branch structure of the cable harness can affect the route of the cable harness layout. The topological structure design of the cable harness is a key to such a layout. In this paper, a novel multi-branch cable harness layout design method is presented, which unites the probabilistic roadmap method (PRM) and the genetic algorithm. First, the engineering constraints of the cable harness layout are presented. An obstacle-based PRM used to construct non-interference and near to the surface roadmap is then described. In addition, a new genetic algorithm is proposed, and the algorithm structure of which is redesigned. In addition, the operation probability formula related to fitness is proposed to promote the efficiency of the branch structure design of the cable harness. A prototype system of a cable harness layout design was developed based on the method described in this study, and the method is applied to two scenarios to verify that a quality cable harness layout can be efficiently obtained using the proposed method. In summary, the cable harness layout design method described in this study can be used to quickly design a reasonable topological structure of a cable harness and to search for the corresponding routes of such a harness.

Dynamic route planning with uncertain information

1996 ◽  
Vol 9 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Forouzan Golsahni ◽  
Enrique Cortes-Rello ◽  
Thomas H. Howell
Keyword(s):  
Route Planning ◽  
Uncertain Information ◽  
Dynamic Route

scholarly journals Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm

2021 ◽  
Vol 1941 (1) ◽  
pp. 012012
Author(s):  
Jie Zhang ◽  
Ningzhou Li ◽  
Danyu Zhang ◽  
Xiaojuan Wei ◽  
Xiaojuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Route Planning ◽  
Improved Genetic Algorithm ◽  
Decision Variables ◽  
Multi Uav

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

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