Implicit robot coordination using Case-Based Reasoning behaviors

Abstract

Multi-robot systems (MRS) are a very active and important research topic nowadays. One of the main problems of these systems is the large number of variables to take into account. Due to this, robot behaviors are sometimes learnt instead of calculated via analytical expressions. A typical learning mechanism, specially for biomimetic robots, is Learning from demonstration (LfD). This paper proposes a LfD approach for implicit coordinated navigation using combination of CaseBased Reasoning (CBR) behaviors. During a training stage, CBR is used to learn simple behaviors that associate positions of other robots and/or objects to motion commands for each robot. Thus, human operators only need to concentrate on achieving their robot's goal as efficiently as possible in the operating conditions. Then, in running stage, each robot will achieve a different coordinate navigation strategy depending on the triggered behaviors. This system has been successfully tested with three Aibo-ERS7 robots in a RobCup-like environment.

ARCog: An Aerial Robotics Cognitive Architecture

“…Several articles follow the evolution of the field into modern applications for storing tasks. For instance, [92] demonstrated the CBR application combined with Learning from Demonstration (LfD) to support a decision in complex multi-robot environments. In industry and business fields, CBR has the most comprehensive set of publications and solutions demonstrating modern applications ranging from oil and gas industry [93] to knowledge representation systems [94].…”

A Human–Robot-Environment Interactive Reasoning Mechanism for Object Sorting Robot

“…the research on CBR mechanism has become a hot topic in the field of artificial intelligence because of its advantages in short reasoning time and easy case learning ability. Some works applied CBR to HRI [1], [2] and multirobot coordinated navigation [3]. Our previous work [4] and [5], applied CBR to the real-time autonomous navigation and obstacle avoidance of a mobile robot.…”