Cooperation of networked multi robot systems using control theory and machine learning

使用控制理论和机器学习的网络化多机器人系统的协作

• 批准号: RGPIN-2022-04277
• 负责人: Givigi, Sidney
• 金额: $ 2.4万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750619
• 关键词: Cooperation; networked; multi; robot; systems

Autonomous vehicles interact among themselves using specialized sensing, perception, communication, and actuation suites tailored to enhance their navigation capabilities in very challenging environments. Nowadays, the uses of autonomous vehicles, robots, and agents is expanding to diverse areas such as self-driving, military missions, agriculture, and production lines, where collaborative activities and interactions with other robots in assisting humans is necessary. However, current methods used for interactions are based on either control theory concepts or statistical machine learning, making the relationship with humans complicated as humans do not perceive situations through probabilistic calculations. Even though these robots can explore unstructured environments, locate themselves, and map their surroundings, they still lack a degree of predictability derived from human understanding of causality. Unfortunately, this investigation is not very advanced in robotics, especially when considering multiple robots. Therefore, the use of robots outside of manufacturing lines and in unstructured settings like homes, schools, and hospitals is still rare, for the inability of robot to be easily understood by humans limit their usability. To perform such activities, it is not sufficient for robots to communicate with humans by using displays or voice synthesizers, but it is necessary that humans intuitively understand what robots do and why. This requires the construction of causal models that are like those used by humans. In other applications, such as the military, these characteristics are also important, for humans must be able to trust that the decisions made by autonomous vehicles and robots follow a cause-and-effect rationale. Impressive as it is, it seems to be very unlikely that this can be achieved using the advances made in statistical machine learning. This research program aims at developing novel techniques based on causal inference systems and machine learning for efficient control of networks of autonomous vehicles and robots. These techniques will take advantage of the communication network established by the vehicles but will be based on the identification of causal structures in the data and in the interactions. The research program also contemplates the use of modern machine learning architectures not only based on data, but also on models that can be understood by humans. New theoretical results will be generated with a range of applications in mind, from human-robot interactions to military applications, with close research collaboration with industry. Analyses of social interactions and dilemmas will be considered as well as classical environments including multiple vehicles pursuing others and games played by children such as capture the flag. A total of six graduate and five undergraduate students will be trained with the support of this grant over the next five years, contributing to Canada's highly qualified personnel.

自动驾驶汽车使用专门的传感、感知、通信和驱动套件进行交互，以增强其在非常具有挑战性的环境中的导航能力。如今，自动驾驶汽车、机器人和代理的用途正在扩展到自动驾驶、军事任务、农业、生产线等多种领域，在这些领域，协作活动和与其他机器人的互动是必要的，以协助人类。然而，目前用于交互的方法要么基于控制论概念，要么基于统计机器学习，这使得与人类的关系变得复杂，因为人类不通过概率计算来感知情况。尽管这些机器人可以探索非结构化的环境，定位自己，绘制周围环境的地图，但它们仍然缺乏人类对因果关系的理解所带来的一定程度的可预测性。不幸的是，这项研究在机器人技术方面并不是很先进，特别是在考虑多个机器人时。因此，机器人在生产线之外以及家庭、学校和医院等非结构化环境中的使用仍然很少，因为机器人无法被人类轻易理解，限制了它们的可用性。为了完成这些活动，机器人仅仅通过显示器或语音合成器与人类交流是不够的，人类有必要直观地理解机器人做什么以及为什么做。这就需要构建类似人类使用的因果模型。在军事等其他应用中，这些特征也很重要，因为人类必须能够相信自动驾驶汽车和机器人做出的决定遵循因果关系。令人印象深刻的是，这似乎不太可能利用统计机器学习的进步来实现。本研究项目旨在开发基于因果推理系统和机器学习的新技术，以有效控制自动驾驶汽车和机器人网络。这些技术将利用车辆建立的通信网络，但将以识别数据和相互作用中的因果结构为基础。该研究项目还考虑使用现代机器学习架构，不仅基于数据，还基于人类可以理解的模型。新的理论成果将与一系列应用相结合，从人机交互到军事应用，与工业界密切研究合作。将考虑社会互动和困境的分析，以及经典环境，包括多辆车追逐其他车辆和儿童玩的游戏，如夺取旗帜。在未来五年内，将有六名研究生和五名本科生在这笔赠款的支持下接受培训，为加拿大的高素质人才做出贡献。