TeCoLa
Manos Koutsoubelias, Spyros Lalis
- Year
- 2016
- Citations
- 21
Abstract
Many pervasive computing applications can benefit from the advanced operational capability and diversity of sensing and actuation resources of modern robotic platforms. Even more powerful functionality can be achieved by combining robots with different mobility capabilities, some of which may already be deployed in the area of interest. However, the current programming frameworks make such flexible resource utilization a daunting task, as the application developer is responsible for the laborious and awkward management of the heterogeneity and transient availability of resources and services, which is done in a manual way. TeCoLa is a programming framework addressing the above issues. It supports structured services as first-class entities, which can appear/disappear dynamically, and are accessed in a transparent way. In addition, to ease the task of multi-robot programming, TeCoLa supports the creation and management of teams based on the dynamic service capability and availability of individual robots. Teams are maintained behind the scenes, without any effort from the application programmer. Furthermore, they can be controlled through team-level service interfaces which are instantiated in an automatic way, based on the services of the robots that are members of the team. We present a first implementation of TeCoLa for a Python environment, and discuss how we test the functionality of our prototype using a software-in-the-loop approach.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002