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同时定位和映射在多机器人系统中的动态环境中,未知初始对应
Hadiseh Malakouti-Khah1, Nargess Sadeghzadeh-Nokhodberiz1, Allahyar Montazeri2
1Department of Control Engineering, Qom University of Technology, Qom, Iran.
Frontiers in robotics and AI
|January 29, 2024
概括
本研究介绍了一种新的去中心化方法,用于动态环境中的多机器人同时定位和映射 (SLAM). 它有效地处理移动的地标和未知的机器人初始位置,以改善映射和定位.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 同时定位和绘制 (SLAM)
- 多代理系统 多代理系统
背景情况:
- 传统的同时定位和映射 (SLAM) 假设静态环境,限制其在现实世界的场景中的应用.
- 现有的动态SLAM方法经常过出移动的地标,这可能导致信息丢失.
- 多机器人系统为大规模,复杂和动态的环境提供了增强的性能,但也带来了协调挑战.
研究的目的:
- 在动态环境中为多机器人SLAM提出一种新的去中心化方法.
- 为了应对未知的初始机器人通信和移动地标的挑战.
- 在动态设置中提高本地化和映射的准确性和稳定性.
主要方法:
- 对于去中心化的多机器人SLAM,采用了修改后的Fast-SLAM算法.
- 该方法明确将移动的地标纳入SLAM过程中.
- 整合了地图对齐和合并的地理方法,解决了未知的初始对应.
- 数据关联是使用每个机器人的SLAM过程中的测量预测来执行的.
主要成果:
- 拟议的去中心化方法在具有多个机器人的动态环境中展示了有效的SLAM.
- 该方法成功处理未知的机器人初始位置,并准确地合并地图.
- 模拟结果验证了所介绍的算法的性能和稳定性.
结论:
- 开发的去中心化多机器人SLAM方法为动态环境提供了可行的解决方案.
- 移动地标处理和地图合并技术的整合提高了本地化和绘图准确性.
- 这种方法提供了一个合作和高效的解决方案,而不需要中央处理单元.
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