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

人们常说机器人最适合干肮脏、枯燥和危险的工作。 该研究计划涉及技术的发展，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。 我们专注于对象聚类和排序的问题。 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簇中。 在对象排序中，有多种类型的对象，目标是形成同质聚类，理想情况下，每种类型一个。 这项工作属于福尔斯域的群体机器人，这是一种方法来设计的多机器人系统，其中的机器人被约束到本地感测和行动。 在群机器人系统中，没有单个机器人负责，这也意味着任何单个机器人的故障不会导致系统的故障。 以前的对象聚类和排序方法只处理形状一致的对象，这些对象很容易识别。 我们有兴趣解决现实世界的挑战，如不容易识别的任意形状的物体。 另一个现实世界的挑战是处理机器人变得过度拥挤和生产力低下的拥堵。 与人类用户打交道并利用他们的反馈来修改群体的行为是我们将解决的另一个重大现实挑战。

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.