Map Merging for Long-term Robotic Autonomy

A Control Architecture for Long-Term Autonomy of Robotic Assistants

A major challenge in deploying service robots into the real world is to design a framework that provides effective, long-term interactions with people. This includes interacting with people in a natural way, dealing with multiple users, and being continually aware of the surroundings. This paper proposes a robot control architecture that addresses these issues. First, it enables the representat...

Distributed Map Merging in a Robotic Network

Abstract— The map merging problem in a robotic network consists of the construction of a global map of the environment using the partial (local) maps individually acquired by the robots. We build on ideas from distributed sensor fusion to provide an algorithmic solution to the map merging problem that uses the local maps as sensor measurements. We use a distributed average consensus algorithm t...

Unsupervised online learning for long-term autonomy

A reliable representation of the environment a robot operates in is vital for solving complex tasks. Models that represent information about objects and their properties are typically trained beforehand using supervised methods. This requires intensive human labeling which makes it time-consuming and results in models that are generally inflexible to changes. We would prefer a robot that can bu...

Real-Time Clustering for Long-Term Autonomy

In the future robots will have to operate autonomously for long periods of time. To achieve this they need to be able to learn directly from their environment without human supervision. The use of clustering methods is one possibility to tackle this challenge. Here we present extensions to affinity propagation, a clustering algorithm proposed by Frey and Dueck [5], which makes it suitable for r...

Rethinking autonomy in long term care.