Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task

Abstract

This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.

Genetic Fuzzy Methodology to Predict Time to Return to Play from Sports-Related Concussion

“…Designing a GFS involves tuning the parameters of the GFS: a) membership functions of the input and output variables, and b) rules in the rule-base that define the relationship between the inputs and outputs. Such GFSs have been developed successfully for collaborative robotics [12,13], simulated air-to-air combat [14], clustering and task planning [15] and a few other applications. As the number of inputs and outputs in the GFS increase, it becomes extremely complex to directly apply GA for tuning GFS parameters.…”