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produce aerial photo and UGV can produce a 3D grid using laser scanners. Another
example is a group of drones that must be coordinated to scan the corridor
ahead of a convoy. This task can’t be done by a single robot and the group should
be automatically fly in formation in a predefined distance from the first truck.
In this paper we show an approach how a single user can control an MRS
in similar situations using BML. The goal of our project was to demonstrate that
the robots of MRS can be coordinated quickly and efficiently by using BML as a command
and report language and using ROS as a communication standard between
different robot systems. We defined a set of commands that should be supported
by the MRS and how they should be implemented to test our approach.
We use a simple hierarchical approach with intelligent node representing groups
of robots and the control nodes for each robot. This means that one intelligent node
receives the command from the user and this node is capable of breaking the command
up into subcommands for all subordinate robots. This allows less coupling
between the robots. Each node must only know how to interpret a command and
what commands are supported by it subordinate units. Having the intelligent nodes
on the robots makes it possible for the robots to be reactive to new situations even
if the connection to the C2-Central is not available.
The paper is structured as the following. In Section 2 some background information
is given about supervisory control, BML and ROS. Section 3 describes
the systems that are used in the project. This includes the graphical user interface
and the robot systems of the Leibniz Universität Hannover and University of Siegen.
Section 4 describes the challenges and benefits using ROS on the robots. The implementation
of commands is described in section 5. A conclusion and an outlook
are given in section 6.
II. Background
A. Supervisory control of Multi-Robot Systems
The goal of our work is to provide supervisory control of Multi-Robot
Systems without excessive human workload. Related work on controlling UAV
Multi-Robot System was done by Cummings and Mitchell [1] and Nehme et
al. [2]. The workload of controlling a UAV Multi-Robot System was analyzed by
Dixon et al. [3].
Quite similar to the supervisory control of Multi-Robot Systems is the supervisory
control of multi-agent systems (MAS) [4]. For that area different approaches
are known. The first one is “control-by-behavior.” In this approach, different
behaviors for each agent are defined and the operator selects one of them.
However, this approach does not scale with larger groups of agents, more behaviors
or more complex behaviors as mentioned by Wilson et al. [5]. Another approach
is the “control-by-policy” approach. Here, the operator can define constraints or