Genetic Fuzzy Methodology for Decentralized Cooperative UAVs to Transport a Shared Payload

Abstract

In this work, we train controllers (models) using Genetic Fuzzy Methodology (GFM) for learning cooperative behavior in a team of decentralized UAVs to transport a shared slung payload. The training is done in a reinforcement learning fashion where the models learn strategies based on feedback received from the environment. The controllers in the UAVs are modeled as fuzzy systems. Genetic Algorithm is used to evolve the models to achieve the overall goal of bringing the payload to the desired locations while satisfying the physical and operational constraints. The UAVs do not explicitly communicate with one another, and each UAV makes its own decisions, thus making it a decentralized system. However, during the training, the cost function is defined such that it is a representation of the team’s effectiveness in achieving the overall goal of bringing the shared payload to the target. By including a penalization term for any constraint violation during the training, the UAVs learn strategies that do not require explicit communication to achieve efficient transportation of payload while satisfying all constraints. We also present the performance metrics by testing the trained UAVs on new scenarios with different target locations and with different number of UAVs in the team.

Adaptive Backstepping Multi-Agent Control of Multiple UAVs Conveying Load: A Graph Rigidity Approach

“…Reference [23] designed a decentralized control of MUAVLs for pick and place tasks involving static and dynamic objects with monocular camera images and an EPM gripper. Reference [24] applied decentralized Genetic Fuzzy Methodology (GFM) for learning cooperative behavior of MUAVLs. However, GFM is cumbersome, due to load scalability issues and the amount of time required to train and test different cases.…”