학술논문

Improving Multi-robot Coordination by Game-Theoretic Learning Algorithms
Document Type
Conference
Source
2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) ICTAI Tools with Artificial Intelligence (ICTAI), 2017 IEEE 29th International Conference on. :417-424 Nov, 2017
Subject
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Games
Task analysis
Robot kinematics
Resource management
Decision making
Optimization
robot team coordination
game theoretic learning
task allocation
learning in games
Language
ISSN
2375-0197
Abstract
Cooperative games-based robot cooperation is analysed for reoccurring scenarios. It is shown that potential games can be used for robot coordination when the robots have a shared objective. By observing each others’ behaviour in similar scenarios, they estimate each other’s expected actions, which they use for their own choice of action. The resulting learning scheme can enable “tuning” of smooth cooperation by task allocation in teams of robots for various goals and in reoccurring scenarios of their environment. The theoretical results and methods are illustrated in simulation.