Robots Organizing Environments: Collective Strategies based on Low-Cost Navigation

机器人组织环境：基于低成本导航的集体策略

• 批准号: RGPIN-2017-06321
• 负责人: Vardy, Andrew
• 金额: $ 1.46万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634336
• 关键词: Robots; Organizing; Environments; Collective; Strategies

It is often stated that robots are best suited for dirty, dull, and dangerous jobs. This research program concerns the development of techniques that would allow a swarm of robots to organize their environment, a task that can easily be described as dirty, dull, and potentially dangerous. We focus on the problems of object clustering and sorting. In object clustering, there is only one type of of object in the environment and the goal is to gather all objects into one cluster. In object sorting, there are multiple types of objects and the goal is to form homogeneous clusters, ideally one for each type. This work falls under the domain of swarm robotics which is an approach to the design of multi-robot systems where the robots are constrained to sense and act locally. In a swarm robotic system, no single robot is in charge which also means that the failure of any single robot does not lead to the failure of the system. Previous approaches to object clustering and sorting only handle uniformly shaped objects which are easily identifiable. We are interested in addressing real-world challenges such as arbitrarily shaped objects which are not readily identifiable. Another real-world challenge is dealing with congestion where the robots become overcrowded and unproductive. Dealing with human users and using their feedback to modify the behaviour of a swarm is another significant real-world challenge that we will address.

人们经常说，机器人最适合从事肮脏、枯燥和危险的工作。这项研究计划关注的是技术的开发，使一群机器人能够组织他们的环境，这项任务很容易被描述为肮脏、枯燥和潜在的危险。重点研究了对象聚类和排序问题。在对象聚类中，环境中只有一种类型的对象，目标是将所有对象聚集到一个集群中。在对象分类中，有多种类型的对象，目标是形成同构的簇，理想情况下每种类型一个。这项工作属于群体机器人领域，群体机器人是一种设计多机器人系统的方法，其中机器人被限制在局部感知和行动。在群体机器人系统中，没有单个机器人负责，这也意味着任何单个机器人的故障都不会导致系统的故障。以前的对象聚类和分类方法仅处理易于识别的均匀形状的对象。我们对解决现实世界的挑战感兴趣，例如难以识别的任意形状的物体。另一个现实世界的挑战是处理拥堵问题，在那里，机器人变得过于拥挤和效率低下。与人类用户打交道并使用他们的反馈来修改蜂群的行为是我们将解决的另一个重要的现实世界挑战。