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.

自动驾驶车辆使用专门的传感、感知、通信和驱动套件进行交互，这些套件旨在增强其在极具挑战性的环境中的导航能力。如今，自动驾驶车辆、机器人和代理的用途正在扩展到自动驾驶、军事任务、农业和生产线等不同领域，在这些领域，与其他机器人协助人类的协作活动和交互是必要的。然而，当前用于交互的方法要么基于控制理论概念，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环境、定位自身并绘制周围环境的地图，但它们仍然缺乏源自人类对因果关系的理解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，在生产线之外以及家庭、学校和医院等非结构化环境中使用机器人仍然很少，因为机器人无法轻易被人类理解，限制了其可用性。要执行此类活动，机器人仅通过使用显示器或语音合成器与人类进行交流是不够的，但人类必须直观地理解机器人做什么以及为什么这样做。这需要构建类似于人类使用的因果模型。在其他应用中，例如军事，这些特征也很重要，因为人类必须能够相信自动驾驶车辆和机器人做出的决策遵循因果原理。尽管令人印象深刻，但利用统计机器学习的进步似乎不太可能实现这一目标。该研究计划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车辆和机器人的网络。这些技术将利用车辆建立的通信网络，但将基于数据和交互中因果结构的识别。该研究计划还考虑使用现代机器学习架构，不仅基于数据，而且基于人类可以理解的模型。通过与工业界的密切研究合作，将产生从人机交互到军事应用等一系列应用的新理论结果。将考虑对社交互动和困境的分析以及经典环境，包括追逐他人的多辆车和儿童玩的游戏，例如夺旗。未来5年，该资助计划将培训6名研究生和5名本科生，为加拿大培养高素质人才做出贡献。