一个多机器人协作操纵框架,用于动态和障碍密集的环境:集成深度学习实时任务执行
Afnan Ahmed Adil1, Saber Sakhrieh2, Jinane Mounsef2
1Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
Frontiers in robotics and AI
|August 14, 2025
概括
本研究介绍了一种多机器人系统,用于复杂环境中的自主任务. 它使用领导者-追随者方法,用于对象检测和实时路径重定向的深度学习,以确保安全导航.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 自主系统需要强大的导航和操纵能力.
- 多机器人协作对于动态环境中的复杂任务至关重要.
- 有效的物体检测和避免碰撞对于移动操纵器至关重要.
研究的目的:
- 介绍一个多机器人协作操纵框架,用于自主执行任务.
- 为了使移动操纵器能够在充满障碍的动态环境中有效运行.
- 开发一个协调完成任务的系统,包括对象识别和运输.
主要方法:
- 实现了领导者-追随者架构,用于机器人团队协调.
- 利用深度学习 (YOLOv2) 通过RGB-D摄像头进行对象检测.
- 基于采样的综合路径规划与2D LiDAR用于避开障碍物和实时重定向.
- 使用 MATLAB/Stateflow 和 ROS 进行系统间通信的管理任务调度和控制.
主要成果:
- 证明了自主执行任务,包括对象识别,挑选和运输.
- 在模拟的动态环境中实现了先进的避免碰撞和实时路径重定向.
- 成功地协调了多个移动操纵器,用于需要联合努力的任务.
- 验证了框架适应不同团队规模和任务复杂性的适应性.
结论:
- 开发的多机器人框架使在具有挑战性的环境中实现高效和安全的自主操作.
- 深度学习,先进路径规划和协作控制的整合增强了任务执行.
- 该系统显示出在物流自动化,协作制造和人机交互等领域的重大应用潜力.
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