Abstract
This study presents an intelligent distribution framework based on edge computing and proposes navigation and obstacle avoidance algorithms for connected logistics vehicles (CLVs) on the basis of Trimble BD982 positioning sensor and tentacle algorithm (TA). An edge computing framework for the distribution of CLVs is established, and the functions of three layers (cloud server, edge equipment, and terminal) are described in detail. The basic functions, hardware, and software systems of the CLV are designed and presented. Focusing on autonomous driving, a Global Positioning System (GPS) navigation algorithm and an obstacle avoidance control strategy on the layer of edge equipment are developed on the basis of the TA. Autonomous GPS navigation is realized by combining the entire road network with the local road network to avoid obstacles. The TA is improved to help the CLV for avoiding obstacles. Experiments show that the hardware system and designed algorithms of the CLV are effective. The tracking error on the straight-line track is within 3 cm, the change rate of longitudinal velocity is within 0.3 g/s, the change rate of tire side deflection angle is less than 1°/s, and the calculation time is shortened by 25% when the calculation time is 30 ms. These results indicate that the vehicle has good stability and performance during obstacle avoidance in real time, and the proposed algorithms are superior to traditional algorithms. The CLV can realize autonomous GPS navigation, with high navigation accuracy, reliable obstacle avoidance performance, and stable vehicle handling.
Highlights
Connected vehicles are capable of automated driving and connectivity with other vehicles or road users, the road infrastructure, and the cloud
To complete the main navigation function of the vehicles relying on Global Positioning System (GPS) and inertial navigation systems, Google fused the cumulative data of the wheel encoder with the GPS data to obtain an accurate location; the 3D radar on the roof and the millimeter wave radar around the body of a vehicle are used to avoid obstacles
We propose the framework of connected logistics vehicles (CLVs) cloud platform based on edge computing and focus on autonomous navigation and obstacle avoidance strategy of CLV
Summary
Connected vehicles are capable of automated driving and connectivity with other vehicles or road users, the road infrastructure, and the cloud. CLVs or similar products, including robots from Alibaba, CaiNiao, Jingdong, YunJi Technology, Amazon, and Savioke, have emerged in the market These robots are all designed for short-distance distribution, especially in closed environments, such as hospitals, hotels, and factories. The robots of YunJi and Savioke are commonly used in buildings to deliver small items (such as toothbrushes and towels) These products are designed for enclosed buildings or areas, and their application is not complex. When the road network is complex and the vehicle serves multiple clients every time, that means the vehicle need go to multiple depots to take parcels and drive autonomously to deliver all the parcels one by one Under such complicated conditions, edge computing provides us a chance to make the distribution platform more efficient.
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