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一个凸起的优化方法多机器人任务分配和路径规划
Tingjun Lei1, Pradeep Chintam1, Chaomin Luo1
1Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究引入了多机器人系统的新框架,以有效地分配任务和计划路径,尽量减少探索任务的行程距离. 该方法优化了机器人团队的部署和动态子任务分配,以提高性能.
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
- 运营研究 运营研究
背景情况:
- 在现实应用中动态部署多个机器人对于探索等任务至关重要.
- 在多机器人任务分配中最大限度地降低距离成本是一个NP难题,需要有效的解决方案.
研究的目的:
- 开发一个基于团队的多机器人任务分配和路径规划的新框架.
- 在探索任务中,尽量减少机器人和它们指定的目标之间的距离.
主要方法:
- 提出了一个基于凸优化的距离最佳模型,以尽量减少旅行距离.
- 该框架集成了任务分解,团队聚类,用于部署的凸式优化,Delaunay三角化改进,以及基于地图的自我组织神经网络 (SOMNN) 用于本地分配和路径规划.
主要成果:
- 拟议的框架有效地融合了多机器人系统的任务分解,分配和路径规划.
- 凸起式优化用于近似机器人团队形状并最大限度地缩小距离,其次是Delaunay三角化用于位置精细化.
- 一个SOMNN范式使机器人团队内的动态子任务分配和本地路径规划成为可能.
结论:
- 开发的混合框架为复杂的多机器人任务分配和路径规划问题提供了有效和高效的解决方案.
- 该方法解决了动态机器人部署中距离最小化的NP-hard性质.
- 模拟研究验证了机器人探索任务中的框架的有效性和效率.
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