• Journal of Internet of Things and Convergence
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• v.10 no.5
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• pp.27-37
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• 2024

In the context of multi-vehicle and multi-task logistics distribution within a park, traditional algorithms are often hindered by high computational complexity and slow convergence rates. Particle Swarm Optimization (PSO) has gained popularity in path planning for autonomous delivery vehicles due to its straightforward algorithmic principles, broad applicability, and comprehensive search capabilities. However, the conventional PSO is susceptible to premature convergence, leading to local optima. To address this, this study incorporates the Tent map into the PSO to enhance the algorithm's global search ability and prevent premature convergence. Benchmark function tests demonstrate that the improved Particle Swarm Optimization algorithm (TPSO), as proposed in this study, exhibits faster convergence and greater accuracy.In the instance verification section, X Park was selected as an example to construct a multi-vehicle and multi-task model for the logistics distribution within the park. The TPSO algorithm proposed in this paper was used to solve the model, and finally, the superiority of the TPSO algorithm was verified through comparative simulation.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.265-275
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• 2023

The purpose of this paper is to devise an optimal route allocation algorithm for automated vehicle(AV) in monitoring quality of road infrastructure to support the road safety. The tasks of an AV in this paper include visiting node-links at least once during its operation and checking status of road infrastructure, and coming back to its depot.. In selecting optimal route, its priority goal is visiting the node-links with higher risks while reducing costs caused by operation. To deal with the problem, authors devised reward maximizing algorithm for AVs. To check its validity, the authors developed simple toy network that mimic node-link networks and assigned costs and rewards for each node-link. With the toy network, the reward maximizing algorithm worked well as it visited the node-link with higher risks earlier then chinese postman route algorithm (Eiselt, Gendreau, Laporte, 1995). For further research, the reward maximizing algorithm should be tested its validity in a more complex network that mimic the real-life.