Internet of Autonomous Vehicles for The Distribution System of Smart Cities Coventry University

Format:

Book

Conference/Event

Author/Creator:

Ma, Shizi, author.

Contributor:

Ma, Zhitao

QI, Zhiguo

Wang, Sheng

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

With the development of internet technology and autonomous vehicles (AVs), the multimodal transportation and distribution model based on AVs will be a typical application paradigm in the smart city scenario. Before AVs carry out logistics distribution, it is necessary to plan a reasonable distribution path based on each customer point, and this is also known as Vehicle Routing Problem (VRP). Unlike traditional VRP, the urban logistics distribution process based on multimodal transportation mode will use a set of different types of AVs, mainly including autonomous ground vehicles and unmanned aerial vehicles (UAVs). It is worth pointing out that there is currently no research on combining the planning of AVs distribution paths with the trajectory planning of UAVs. To address this issue, this article establishes a bilevel programming model. The upper-level model aims to plan the optimal delivery plan for AVs, while the lower-level model aims to plan a driving trajectory for UAVs. Furthermore, this paper proposes an improved heuristic algorithm for the bilevel programming model. Due to the tendency of Group Search Optimizer (GSO) algorithm to fall into local optima during the process of solving large-scale complex problems, this paper designs an improved GSO algorithm based on improved strategies such as spiral search strategy. During the solving process, based on the upper-level model and using the IGSO algorithm, the distribution order of AVs can be directly solved. In the process of trajectory planning based on the lower-level model, the RRT algorithm is first used to plan an initial trajectory for the UAV. Furthermore, the IGSO algorithm is used to further optimize this trajectory, ultimately achieving the delivery task of the UAV. Finally, a simulation experiment was conducted to compare the effectiveness of the proposed scheme with other algorithms

Notes:

Vendor supplied data

Publisher Number:

2024-01-2882

Access Restriction:

Restricted for use by site license