当前位置: X-MOL 学术IEEE Aerosp. Electron. Syst. Mag. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Path Planning Using Probability Tensor Flows
IEEE Aerospace and Electronic Systems Magazine ( IF 3.6 ) Pub Date : 2021-01-01 , DOI: 10.1109/maes.2020.3032069
Francesco A. N. Palmieri , Krishna R. Pattipati , Giovanni Fioretti , Giovanni Di Gennaro , Amedeo Buonanno

Probability models are emerging as a promising framework to account for “intelligent” behavior. In this article, probability propagation is discussed to model agent's motion in potentially complex grids that include goals and obstacles. Tensor messages in the state-action space (due to grid structure, states are 2-D and the concomitant probability distributions are represented by 3-D arrays), propagated bi-directionally on a Markov chain, provide crucial information to guide the agent's decisions. The discussion is carried out with reference to a set of simulated grids and includes scenarios with multiple goals and multiple agents. The visualization of the tensor flow reveals interesting clues about how decisions are made by the agents. The emerging behaviors are very realistic and demonstrate great potential for the application of this framework to real environments.

中文翻译:

使用概率张量流进行路径规划

概率模型正在成为解释“智能”行为的有前途的框架。在本文中,讨论了概率传播以在包括目标和障碍的潜在复杂网格中对代理的运动进行建模。状态-动作空间中的张量消息(由于网格结构,状态是二维的,伴随的概率分布由 3D 数组表示),在马尔可夫链上双向传播,提供指导代理决策的关键信息. 讨论是参考一组模拟网格进行的,包括具有多个目标和多个代理的场景。张量流的可视化揭示了有关代理如何做出决策的有趣线索。
更新日期:2021-01-01
down
wechat
bug