Scholarlink

The use of autonomous robots is rapidly increasing in industries, requiring advanced navigation strategies to ensure safe operations. SWARM robotics, inspired by natural swarm intelligence, faces critical challenges in collision detection and avoidance. Traditional rule-based methods often struggle in dynamic environments, whereas neural network-based approaches provide adaptive solutions. This paper presents a CNN-based collision detection and avoidance system utilizing LiDAR data for SWARM multi-robot systems. The proposed model was trained and tested using simulated data in the Gazebo and ROS 2 environments. A comprehensive description of the data set, a detailed model architecture, training parameters, and evaluation metrics are provided in this paper. The results show a precision of 85.65% in collision detection, with the precision, recall, and F1-score reported to better understand the performance. In addition, we compare our approach with baseline rule-based methods to highlight improvements.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Collision Detection and Avoidance Among SWARM Robots Using Convolutional Neural Networks (CNNs) in a Harsh Environment

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Shah-Alam Hossain,
  • Md Obaydullah Al Numan,
  • Md Ali Haider,
  • Raihan Kabir

摘要

The use of autonomous robots is rapidly increasing in industries, requiring advanced navigation strategies to ensure safe operations. SWARM robotics, inspired by natural swarm intelligence, faces critical challenges in collision detection and avoidance. Traditional rule-based methods often struggle in dynamic environments, whereas neural network-based approaches provide adaptive solutions. This paper presents a CNN-based collision detection and avoidance system utilizing LiDAR data for SWARM multi-robot systems. The proposed model was trained and tested using simulated data in the Gazebo and ROS 2 environments. A comprehensive description of the data set, a detailed model architecture, training parameters, and evaluation metrics are provided in this paper. The results show a precision of 85.65% in collision detection, with the precision, recall, and F1-score reported to better understand the performance. In addition, we compare our approach with baseline rule-based methods to highlight improvements.