Model-driven design space exploration for multi-robot systems in simulation

被引:2
作者
Harbin, James [1 ]
Gerasimou, Simos [1 ]
Matragkas, Nicholas [2 ]
Zolotas, Thanos [3 ]
Calinescu, Radu [1 ]
Santana, Misael Alpizar [1 ]
机构
[1] Univ York, Dept Comp Sci, York, N Yorkshire, England
[2] Univ Paris Saclay, List, CEA, F-91120 Palaiseau, France
[3] Liverpool John Moores Univ, Sch Comp Sci & Math, Liverpool, Merseyside, England
基金
欧盟地平线“2020”;
关键词
MRS; Multi-robot systems; Model-driven engineering; MDE; Simulation; Design-space exploration; OPTIMIZATION; BEHAVIOR; AUTONOMY;
D O I
10.1007/s10270-022-01041-w
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic design space exploration and robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission and facilitates the specification of the team's intelligence. We evaluate ATLAS and demonstrate its effectiveness in three simulated case studies: a healthcare Turtlebot-based mission and two unmanned underwater vehicle missions developed using the Gazebo/ROS and MOOS-IvP robotic platforms, respectively.
引用
收藏
页码:1665 / 1688
页数:24
相关论文
共 73 条
[51]  
Okamura AM, 2010, IEEE ROBOT AUTOM MAG, V17, P26, DOI 10.1109/MRA.2010.937861
[52]  
Palesi M, 2002, CODES 2002: PROCEEDINGS OF THE TENTH INTERNATIONAL SYMPOSIUM ON HARDWARE/SOFTWARE CODESIGN, P67, DOI 10.1109/CODES.2002.1003603
[53]  
Paraschos A., 2012, P 11 INT C AUTONOMOU, P171
[54]  
Parker L.E., 2012, Reliability and Fault Tolerance in Collective Robot Systems
[55]  
Parker LE, 2016, SPRINGER HANDBOOK OF ROBOTICS, P1335
[56]  
Pinciroli C, 2016, 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), P3794, DOI 10.1109/IROS.2016.7759558
[57]  
Pohl K., 2005, Software Product Line Engineering-Foundations. Principles, DOI DOI 10.1007/3-540-28901-1
[58]   Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy [J].
Rossi, Federico ;
Bandyopadhyay, Saptarshi ;
Wolf, Michael ;
Pavone, Marco .
IFAC PAPERSONLINE, 2018, 51 (12) :112-117
[59]  
Runeson P., 2012, Case Study Research in Software Engineering: Guidelines and Examples
[60]   Probabilistic Multiknob High-Level Synthesis Design Space Exploration Acceleration [J].
Schafer, Benjamin Carrion .
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2016, 35 (03) :394-406