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Applied Sciences
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AI-Based Methods for Object Detection and Path Planning
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AI-Based Methods for Object Detection and Path Planning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 4390

Special Issue Editor

Prof. Dr. Ping Lou

E-Mail Website
Guest Editor
School of Information, Wuhan University of Technology, Wuhan 430070, China
Interests: industrial big data; industrial intelligence; digital twins; multi-agent modeling and simulation

Special Issue Information

Dear Colleagues,

With the advent of advanced technology, artificial intelligence (AI) is being widely used across various domains, with significant applications in object detection and path planning, including warehousing logistics, urban transportation, military operations, automatic driving, robotics, and drones. Through the integration of advanced AI technologies, including reinforcement learning, graph neural networks, multi-agent systems, and large language models, these methods significantly enhance the accuracy, efficiency, and adaptability of object detection and path planning in complex environments.

We are therefore interested in articles that investigate AI-based methods for object detection and path planning. Potential topics include, but are not limited to, the following:

  1. Path optimization in multimodal transportation systems.
  2. Path planning in dynamic environments and under uncertainty.
  3. The application of reinforcement learning in path planning in complex environments.
  4. Coordinated path planning in multi-agent systems.
  5. Spatio-temporal graph neural networks for path planning.
  6. The uses of large language models in path planning and object detection.
  7. Advanced artificial intelligence technology in multi-modal object detection.
  8. Advanced artificial intelligence technology in real-time object detection.
  9. The applications of advanced AI-based object detection and path planning in complex environments such as warehousing logistics, urban transportation, military applications, automatic driving, robotics, and drones.

Prof. Dr. Ping Lou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • multi-agent systems
  • path planning
  • graph neural networks
  • large language models
  • real-time object detection
  • multi-modal object detection

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Published Papers (5 papers)

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Research

29 pages, 1734 KiB  
Article
A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
by Xiaomei Zhang, Xinchen Dai, Ping Lou and Jianmin Hu
Appl. Sci. 2025, 15(9), 4995; https://doi.org/10.3390/app15094995 - 30 Apr 2025
Viewed by 175
Abstract
With the rapid development of e-commerce, the importance of logistics distribution is becoming increasingly prominent. In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service [...] Read more.
With the rapid development of e-commerce, the importance of logistics distribution is becoming increasingly prominent. In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. In the process of last-mile delivery, flexible delivery locations and variable delivery times are effective means to improve customer satisfaction. Therefore, this paper introduces a Vehicle Routing Problem with flexible time windows and delivery locations, considering customer satisfaction (VRP-CS), which considers customer satisfaction by using prospect theory from two aspects: the flexibility of delivery time and delivery locations. This VRP-CS is formally modeled as a bi-objective optimization problem, which is an NP-hard problem. To solve this problem, a Multi-Strategy Adaptive Large Neighborhood Search (MSALNS) method is proposed. Operators guided by strategies such as backtracking and correlation are introduced to create different neighborhoods for ALNS, thereby enriching search diversity. In addition, an acceptance criterion inspired by simulated annealing is designed to balance exploration and exploitation, helping the algorithm avoid being trapped in local optima. Extensive numerical experiments on generated benchmark instances demonstrate the effectiveness of the VRP-CS model and the efficiency of the proposed MSALNS algorithm. The experiment results on the generated benchmark instances show that the total cost of the VRP-CS is reduced by an average of 14.22% when optional delivery locations are utilized compared to scenarios with single delivery locations. Full article
(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)
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17 pages, 6398 KiB  
Article
Integrated Optimization of Emergency Evacuation Routing for Dam Failure-Induced Flooding: A Coupled Flood–Road Network Modeling Approach
by Gaoxiang An, Zhuo Wang, Meixian Qu and Shaohua Hu
Appl. Sci. 2025, 15(8), 4518; https://doi.org/10.3390/app15084518 - 19 Apr 2025
Viewed by 302
Abstract
Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is [...] Read more.
Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is essential to study flood evacuation route planning. This study aimed to minimize evacuation time and reduce risks to personnel by considering the dynamic evolution of dam-break floods. Using aerial photography from an unmanned aerial vehicle, the downstream road network of a reservoir was mapped. A coupled flood–road network coupling model was then developed by integrating flood propagation data with road network information. This model optimized evacuation route planning by combining the dynamic evolution of flood hazards with real-time road network data. Based on this model, a flood evacuation route planning method was proposed using Dijkstra’s algorithm. This methodology was validated through a case study of the Shanmei Reservoir in Fujian, China. The results demonstrated that the maximum flood level reached 18.65 m near Xiatou Village, and the highest flow velocity was 22.18 m/s near the Shanmei Reservoir. Furthermore, evacuation plans were developed for eight affected locations downstream of the Shanmei Reservoir, with a total of 13 evacuation routes. These strategies and routes resulted in a significant reduction in evacuation time and minimized the risks to evacuees. The life-loss risk was minimized in the evacuation process, and all evacuees were able to reach safe locations. These findings confirmed that the proposed method, which integrated flood dynamics with road network information, ensured the safety and effectiveness of evacuation routes. This approach met the critical needs of emergency management by providing timely and secure evacuation paths in the event of dam failure. Full article
(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)
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26 pages, 2528 KiB  
Article
Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
by Xiaomei Zhang, Ziang Liu, Jialiang Zhang, Yuhang Zeng and Chuannian Fan
Appl. Sci. 2025, 15(6), 3035; https://doi.org/10.3390/app15063035 - 11 Mar 2025
Cited by 1 | Viewed by 571
Abstract
With the increasingly busy transportation of cargo at container terminals (CTs), the requirements for terminal throughput and operational efficiency are constantly increasing. The operational efficiency and cost of CTs are closely related to the seamless docking of terminal facilities, especially the joint operation [...] Read more.
With the increasingly busy transportation of cargo at container terminals (CTs), the requirements for terminal throughput and operational efficiency are constantly increasing. The operational efficiency and cost of CTs are closely related to the seamless docking of terminal facilities, especially the joint operation between berths and quay cranes (QCs). Therefore, a joint allocation problem of berths and QCs (BACASP) is presented in this paper and formalized as a mathematical model to minimize terminal operation costs and shipowner dissatisfaction. Given that BACASP is an NP-hard problem, an improved multi-objective cuckoo search (IMOCS) algorithm is proposed to solve this problem, in which an elite-guided tangent flight strategy is presented to speed up the convergence for making up the lack of random search direction of the traditional cuckoo search algorithm; and an information-enhanced abandonment strategy is put forward to increase the possibility of escaping from local optima. Numerical experimental results show the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)
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17 pages, 12340 KiB  
Article
Autonomous Robot Goal Seeking and Collision Avoidance in the Physical World: An Automated Learning and Evaluation Framework Based on the PPO Method
by Wen-Chung Cheng, Zhen Ni, Xiangnan Zhong and Minghan Wei
Appl. Sci. 2024, 14(23), 11020; https://doi.org/10.3390/app142311020 - 27 Nov 2024
Viewed by 1488
Abstract
Mobile robot navigation is a critical aspect of robotics, with applications spanning from service robots to industrial automation. However, navigating in complex and dynamic environments poses many challenges, such as avoiding obstacles, making decisions in real-time, and adapting to new situations. Reinforcement Learning [...] Read more.
Mobile robot navigation is a critical aspect of robotics, with applications spanning from service robots to industrial automation. However, navigating in complex and dynamic environments poses many challenges, such as avoiding obstacles, making decisions in real-time, and adapting to new situations. Reinforcement Learning (RL) has emerged as a promising approach to enable robots to learn navigation policies from their interactions with the environment. However, application of RL methods to real-world tasks such as mobile robot navigation, and evaluating their performance under various training–testing settings has not been sufficiently researched. In this paper, we have designed an evaluation framework that investigates the RL algorithm’s generalization capability in regard to unseen scenarios in terms of learning convergence and success rates by transferring learned policies in simulation to physical environments. To achieve this, we designed a simulated environment in Gazebo for training the robot over a high number of episodes. The training environment closely mimics the typical indoor scenarios that a mobile robot can encounter, replicating real-world challenges. For evaluation, we designed physical environments with and without unforeseen indoor scenarios. This evaluation framework outputs statistical metrics, which we then use to conduct an extensive study on a deep RL method, namely the proximal policy optimization (PPO). The results provide valuable insights into the strengths and limitations of the method for mobile robot navigation. Our experiments demonstrate that the trained model from simulations can be deployed to the previously unseen physical world with a success rate of over 88%. The insights gained from our study can assist practitioners and researchers in selecting suitable RL approaches and training–testing settings for their specific robotic navigation tasks. Full article
(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)
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11 pages, 1668 KiB  
Article
Development of Traffic Scheduling Based on TSN in Smart Substation Devices
by Xin Mei, Jin Wang, Chang Liu, Chang Liu, Jiangpei Xu, Zishang Cui, Lijun Peng and Bing Chen
Appl. Sci. 2024, 14(22), 10135; https://doi.org/10.3390/app142210135 - 5 Nov 2024
Viewed by 1249
Abstract
Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based [...] Read more.
Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the “three-layer and two-network” model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems. Full article
(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)
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