The life expectancy of people is increasing, therefore more professional caregivers are required to take care of the elderly. This results in more expensive health care. A desire for assistive technologies in health care is therefore growing to provide the caregivers to work in a more efficient way. Such assistive technologies are researched and developed at the robotics lab of Eindhoven University of Technology (TU/e) in the form of service robots.
An example of such a service robot is the Autonomous Mate for IntelliGent Operations (AMIGO) robot developed at the robotics lab of the TU/e. A picture of this robot can be seen in the figure below. It is a mobile robot with two arms. It could use its arms, for example, to pick up a drink for a patient and then hand it to the patient.
Research efforts are made to combine these robots with intelligent environments. An example of this is the research project described in my thesis. The idea is to have several service robots in an environment, such as a health care institution, and to have them controlled by a central control system. Therefore, a ubiquitous robotic framework for multi-robot planning and control is proposed. This framework integrates with existing design efforts of the open-source robotics community. The main focus of my research project is on the planning module of the proposed system.
For such a multi-robot control system, a planning module is needed to provide for flexibility. The system needs to be flexible so that it can be used in various different environments and with different types of robots. This planning module handles the planning and scheduling of multiple tasks for the robots in an intelligent way. Therefore, the field of automated planning for robotic systems is analyzed first in my thesis. This is followed by a literature study on subjects ranging from automated planning, to Semantic Web and robotic technologies, such that all fields of ubiquitous robotics are covered. Based on the analysis and the literature study requirements are formed for the system. These requirements are then used to create the design of the framework. Furthermore, a prototype of the framework is implemented and experiments are performed with this prototype. The results of these experiments are evaluated. The thesis also includes a comparison between the proposed system and other similar planning systems as part of the discussion after the evaluation.
The design of the framework includes technologies such as Robot Operating System (ROS), Semantic Web Ontology languages (OWL and OWL-S), RoboEarth Cloud Engine (Rapyuta), and MIndiGolog, as they are innovative, provide genericness and integrate well with each other. MIndiGolog is a high-level agent programming language for multi-agent systems, and is based on situation calculus, a logic formalism designed for representing and reasoning about dynamic domains. MIndiGolog can reason over world states and transitions between states. In the proposed system, robots are also required to have a robot-type description available, which describes the specific robot capabilities of each connected robot, so that the planner can select the right robot for specific tasks. The planner searches for plans to accomplish user-given tasks.
The programming languages Python and Prolog are used to implement the prototype of the framework. PySWIP is used to query Prolog rules in Python. The prototype includes an executive layer, a planning layer and an ontology layer. The figure below displays these three layers. My thesis focuses on the planning layer.
Several qualitative tests and an experiment with real robots are performed with the implemented prototype. The tests are performed to validate the functionality of the planning module of the system. The experiment describes a use case in which robots need to help with a cocktail party. In this use case, the robots need to take orders from guests, bring drinks to the guests, find empty drinks, and clean up empty glasses. The performed experiment however only includes the subtask of detecting empty drinks, due to limited time and resources. The robots need to navigate to the location where they expect empty drinks to be found and then perceive the empty drink.
The picture below shows the experiment. As can be seen, two service robots stand in the middle of a room. The robots need to drive to a location where they expect empty drinks to be found and then perceive this drink. The planning module needs to select the closest robot to go to the closest drink. A video of the experiment is available online.
The results of both the tests and the experiment are successful, as the tests performed as expected and the robots navigated to the nearest location where they expect empty drinks to be found in the experiment. There were however some performance issues with the communication interface of the system, including latency and bandwidth issues. Based on the results, the proposed framework is evaluated as a feasible approach to a ubiquitous robotic system for multi-robot planning and control.
