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FRIEND::Architecture - MASSiVE

MASSiVE - Hybrid multi-layer control architecture for semi-autonomous service robots with verified task execution.
The realization of semi-autonomy is based on the central idea to include human tasks into task execution. A good explanation of this principle is given in the following quotation:

"When we think of interfaces between human beings and computers, we usually assume that the human being is the one requesting that a task be completed, and the computer is completing the task and providing the results. What if this process were reversed and a computer program could ask a human being to perform a task and return the results? [Mechanical Turk - amazon.com]"

With respect to a semi-autonomous service robotic system this means that the user's cognitive capabilities are taken into account, whenever a robust and reliable technical solution is unavailable. It is obvious that the acceptance of such a system will be low in general. But for people that are dependent on a personnel assistant, like the disabled or elderly, this approach offers the opportunity to decrease this dependency and therefore to increase their quality of life. A service robot has to be able to pursue a certain mission goal as commanded from its user but also needs to react flexibly to dynamic changes within the workspace. To meet these requirements, hybrid multi-layer control architectures have been successfully applied [5] [6]. These architectures usually consist of three layers:
  • A deliberative layer, which contains a task planner to generate a sequence of operations to reach a certain goal with respect to the user's input command.

  • A reactive layer, which has access to the system's sensors and actuators and provides reactive behavior which is robust even under environmental disturbances, for example with the help of closed-loop control.

  • A sequencer that mediates between deliberator and reactive layer i.e. activates or deactivates reactive operations according to the deliberator's specification.
Task knowledge specification, verification and execution concept in MASSiVE.
The software framework MASSiVE (Multi-layer Architecture for Semi-autonomous Service robots with Verified task Execution) [7] [8] is a special kind of hybrid multi-layer control architecture which is tailored to the requirements of semi-autonomous and distributed systems, like the care-providing robot FRIEND, acting in environments with distributed smart components. These intelligent wheelchair mounted manipulator systems allow to benefit from the inclusion of the user's cognitive capabilities into task execution and consequently lower the system complexity compared to a fully autonomous system. The semi-autonomous control requires a sophisticated integration of a human-machine-interface (HMI) which is able to couple input devices according to the user's impairment [9], for example a haptic suit, eye-mouse, speech-recognition, chin joystick or a brain-computer interface (BCI) [10] [11]. In the resulting MASSiVE control architecture with special emphasis on the HMI component the deliberator has been moved to the sequencer component, and the HMI has direct access to control the actuators in the reactive layer during user interactions (e.g., to move the camera, until the desired object to be manipulated is in field of view).

Besides the focus on semi-autonomous system control, the MASSiVE framework includes a second main paradigm, namely the pre-structuring of task knowledge. This task planner input is specified offline in a scenario and model driven approach with the help of so-called process-structures on two levels of abstraction, the abstract level and the elementary level [12]. After specification and before being used for task execution, the task knowledge is verified offline, to guarantee a robust runtime behavior. This development process model provides a structured guidance and enforce consistency throughout the whole process, so that uniform implementations and maintainability are achieved. Furthermore it guides through development and test of system core functionality (skills).

Human Machine Interface (HMI) of care-providing robot FRIEND.
The tasks is selected and started by the user via the HMI, on a high level of abstraction, e.g. "cook meal". After initial monitoring to define the current state of the system and the environment, the tasks execution is performed and a list of elementary operations are created which can be executed autonomously by the system. These elementary operations consists of, e.g. image processing algorithm to recognize objects in the environment or manipulative algorithms to calculate a special trajectory to grasp an object.

Besides these layers, a world model is included in the control architecture that contains the current system's perspective on the world according to the task to be executed. Due to the hybrid architecture a separation of world-model data into two categories is mandatory: The deliberator operates with symbolic object representations (e.g. "C" for the representation of a cup), while the reactive layer deals with the sensor percepts taken from these objects, so-called sub-symbolic information. Examples for sub-symbolic information are the color, size, shape, location or weight of an object.
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