The Development of a Semi-Autonomous Framework for Personal Assistant Robots - SRS Project

Introduction

SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the initial demonstration platform. SRS is designed to enable a robot to act as a shadow of its controller and to perform multiple functions. For example, elderly parents can have a robot as a shadow of their children or caregivers. In this case, adult children or caregivers can help them remotely and physically with tasks such as fetching medication or checking around the house as if the children or caregivers were resident at home. This article is expanded from an early article published by Qiu et al. (2012) during the project development period.

The SRS hardware platform is based on the Care-O-bot 3 from Fraunhofer IPA. Care-O-bot 3 is a mobile assistant robot able to move safely among humans, to detect and grasp typical household objects, and to safely exchange them with humans (Graf et al., 2009). The SRS R&D has been focused on an autonomous robot control structure and human-robot interaction for more robust robot operation. For tasks that cannot be performed by robots autonomously but can be executed remotely, a robot can try and support the remote operator as much as possible. To support the framework, user interfaces will enable the SRS solution to be accessed by average users in real life settings (See Figure 1).

Figure 1.

Care-O-bot 3 testing for SRS in a home environment

It goes beyond the architecture presented in Qiu et al. (2012) and Bohren et al. (2011) by prototyping an improved and integrated task planning and coordination system for unstructured environments. The proposed framework is validated and tested using ROS (Robotic Operating System) (Quigley et al., 2009).

For an autonomous control framework to operate in unstructured environments, there are two major challenges that must be overcome:

• 1.

  The first challenge is how to handle uncertainties in the unstructured environment. Autonomous systems require a well-defined strategy for coordination. The well-defined strategy is only available when the environment is structured. Borrowing the idea of local linearization from nonlinear systems, a possible workaround could be achieved through estimating a virtually structured environment and dynamically adjusting the control strategy. This idea is realised by an automatic task planner, which initialises proactive actions on the symbolic level. A task coordination mechanism maintains autonomous reactive behaviours. The proactive movements on the symbolic level tend to be some general plans for a range of similar tasks. They are normally not sensitive to uncertainty. If the plans are decided, a robot would know what to do at the lower level based on the structured task coordination strategy. The high-level task planner alone could then focus on updating the world model and revising the symbolic plans.

• 2.

  The second challenge is how to enable the extension and reuse of existing capabilities for future applications. The research presented is not intended to build a fully capable general purpose autonomous system, as it is still a long-term goal of technical development. Instead, this work is targeted at a scalable autonomous control framework which could efficiently integrate a large set of useful capabilities. As proposed in Bohren et al. (2011), it uses the “app-store” paradigm. Their attempts focused primarily on the reuse of general purpose robotic sub-systems such as navigation and detection. In this work we explore the possibility to go a step further by also integrating high-level semantic knowledge, task planning, and symbolic grounding rules into the framework.