对于杂乱的环境中的异质UAV-UGV团队,安全的分组扩展和运动规划
Qian Chen1,2, Xiufeng Liu3, Wendi Nie2
1Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China.
这项研究引入了一种用于控制异质无人机和地面机器人组成的新型框架,使在复杂环境中实现稳定,无碰撞的导航. 这种方法确保了自主任务的持续组成完整性和高效机动.
科学领域:
- 机器人技术和自主系统
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 不同类型的无人机 (UAV) 和无人地面飞行器 (UGV) 构造为自主任务带来了显著的优势.
- 挑战包括保持稳定的3D形成控制,在密集的障碍物中无碰撞导航,以及具有异质动态和干扰的实时计算.
研究的目的:
- 为异质多UAV-UGV系统提出一个集成形成控制和运动规划的分布式框架.
- 为了应对在避免障碍时保持阵容完整性的挑战,使垂直协调,并支持动态重新配置.
- 确保在狭窄空间和动态环境中安全航行.
主要方法:
- 一个基于分布式刚性图的自适应安全人工潜力场 (RG-ASAPF) 框架.
- 基于后退的自适应式形成控制器,使用刚性图形理论进行距离共识.
- 具有在线排斥调节的自适应安全人工潜力场,可实现光滑,无碰撞的轨迹.
- 稳定性保证和安全认证的备用机制的利亚普诺夫分析.
主要成果:
- 在避免障碍时,RG-ASAPF框架成功地保持了连续的形成完整性.
- 实现了UAV和UGV层之间的垂直协调,并设置了规定的间距.
- 演示了动态阵列重新配置和在狭窄通道中安全导航.
- 模拟显示在计算效率,稳定性和能源消耗方面比现有方法具有更高的性能.
结论:
- 拟议的RG-ASAPF框架有效地整合了异质多UAV-UGV系统的形成控制和运动规划.
- 它为复杂环境中的自主任务提供了增强的稳定性,效率和稳定性.
- 该方法适用于各种应用,如灾难响应,监视和合作物流.
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