A Fast Formation Obstacle Avoidance Algorithm for Clustered UAVs Based on Artificial Potential Field
AEROSPACE SCIENCE AND TECHNOLOGY（2024）
Nanjing Univ Informat Sci & Technol
Abstract
The aim of this paper is to improve the rapid obstacle avoidance control of UAVs cluster in a complex obstacle environment, primarily utilizing the finite-time consistent formation control algorithm and the improved artificial potential field algorithm to design the fast obstacle avoidance control strategy. Firstly, a finite-time consistent formation control algorithm is adopted to address the problems of slow formation speed and low control accuracy of UAVs clusters for establishing the formation model and control of UAVs cluster. Then, taking static and dynamic obstacles as obstacle avoidance targets, the improved artificial potential field algorithm is utilized, and the auxiliary potential field and dynamic situation field range of obstacle velocity are also introduced. The algorithm enhances obstacle avoidance speed and efficiency from the two aspects: time optimization and space optimization. Meanwhile, dynamic perturbation is introduced to address the local minimum problem of traditional artificial potential field. Finally, the effectiveness of the algorithm is confirmed through simulation on a verification platform and testing on a physical prototype verification platform.
MoreTranslated text
Key words
Auxiliary potential field,Finite time consistency,Artificial potential field,Formation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined