Optimized Path Planning for Logistics Robots Using Ant Colony Algorithm under Multiple Constraints

With the rapid development of the logistics industry, the path planning of logistics vehicles has become increasingly complex, requiring consideration of multiple constraints such as time windows, task sequencing, and motion smoothness. Traditional path planning methods often struggle to balance these competing demands efficiently. In this paper, we propose a path planning technique based on the Ant Colony Optimization (ACO) algorithm to address these challenges. The proposed method optimizes key performance metrics, including path length, task completion time, turning counts, and motion smoothness, to ensure efficient and practical route planning for logistics vehicles. Experimental results demonstrate that the ACO-based approach outperforms traditional methods in terms of both efficiency and adaptability. This study provides a robust solution for logistics vehicle path planning, offering significant potential for real-world applications in dynamic and constrained environments.

Prioritized experience replay-based DDQN for Unmanned Vehicle Path Planning

Path planning module is a key module for autonomous vehicle navigation, which directly affects its operating efficiency and safety. In complex environments with many obstacles, traditional planning algorithms often cannot meet the needs of intelligence, which may lead to problems such as dead zones in unmanned vehicles. This paper proposes a path planning algorithm based on DDQN and combines it with the prioritized experience replay method to solve the problem that traditional path planning algorithms often fall into dead zones. A series of simulation experiment results prove that the path planning algorithm based on DDQN is significantly better than other methods in terms of speed and accuracy, especially the ability to break through dead zones in extreme environments. Research shows that the path planning algorithm based on DDQN performs well in terms of path quality and safety. These research results provide an important reference for the research on automatic navigation of autonomous vehicles.

Automatic News Generation and Fact-Checking System Based on Language Processing

This paper explores an automatic news generation and fact-checking system based on language processing, aimed at enhancing the efficiency and quality of news production while ensuring the authenticity and reliability of the news content. With the rapid development of Natural Language Processing (NLP) and deep learning technologies, automatic news generation systems are capable of extracting key information from massive data and generating well-structured, fluent news articles. Meanwhile, by integrating fact-checking technology, the system can effectively prevent the spread of false news and improve the accuracy and credibility of news. This study details the key technologies involved in automatic news generation and factchecking, including text generation, information extraction, and the application of knowledge graphs, and validates the effectiveness of these technologies through experiments. Additionally, the paper discusses the future development directions of automatic news generation and fact-checking systems, emphasizing the importance of further integration and innovation of technologies. The results show that with continuous technological optimization and practical application, these systems will play an increasingly important role in the future news industry, providing more efficient and reliable news services.