Abstract
This demo concerns the open source physics-driven simulation
tool Multi Agent Exploration Simulator (MAES), which
can be used for developing and evaluating exploration and
coverage algorithms in an unknown continuous 2D environment.
MAES supports development efforts for new algorithms
by means of debugging tools for developed algorithms.
It provides a fully user-controlled camera over the environment
under exploration, which can be attached to a single
robot to follow its operations. MAES represents graphically
the SLAM process and the areas explored by one or all robots,
to visualize exploration, coverage, heatmap, slam etc, both
for a specific robot or for the entire swarm. Finally, MAES
provides both a simple interface for developing algorithms
in C# as well as a Robot Operating System 2 (ROS2) interface,
the latter allowing to integrate with existing robot controllers.
MAES aims to bridge the gap between unrealistic,
simple simulations, usually executed on grid environments,
and heavy, time consuming, but realistic simulations, such as
ARGoS or Gazebo. The accompanying video is available at
https://youtu.be/0RzPN0oW7v8. MAES’ source code can be
found at https://github.com/MalteZA/MAES.
tool Multi Agent Exploration Simulator (MAES), which
can be used for developing and evaluating exploration and
coverage algorithms in an unknown continuous 2D environment.
MAES supports development efforts for new algorithms
by means of debugging tools for developed algorithms.
It provides a fully user-controlled camera over the environment
under exploration, which can be attached to a single
robot to follow its operations. MAES represents graphically
the SLAM process and the areas explored by one or all robots,
to visualize exploration, coverage, heatmap, slam etc, both
for a specific robot or for the entire swarm. Finally, MAES
provides both a simple interface for developing algorithms
in C# as well as a Robot Operating System 2 (ROS2) interface,
the latter allowing to integrate with existing robot controllers.
MAES aims to bridge the gap between unrealistic,
simple simulations, usually executed on grid environments,
and heavy, time consuming, but realistic simulations, such as
ARGoS or Gazebo. The accompanying video is available at
https://youtu.be/0RzPN0oW7v8. MAES’ source code can be
found at https://github.com/MalteZA/MAES.
Originalsprog | Engelsk |
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Publikationsdato | 13 jun. 2022 |
Antal sider | 2 |
Status | Udgivet - 13 jun. 2022 |
Begivenhed | The 32nd International Conference on Automated Planning and Scheduling - Virtual, Singapore, Singapore Varighed: 13 jun. 2022 → 24 jun. 2022 |
Konference
Konference | The 32nd International Conference on Automated Planning and Scheduling |
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Lokation | Virtual |
Land/Område | Singapore |
By | Singapore |
Periode | 13/06/2022 → 24/06/2022 |