Development of swarm robot system for cooperative marine tasks

Abstract

As an advance of robot technologies, a number of autonomous robot systems have been developed to perform works in various environments. Recently, the interest in and needs for the unmanned vehicle-based robot systems for the purposes of human safety and convenience are growing. Also, needs for unmanned vehicles on behalf of human in hazardous or inaccessible circumstances including aerial, underground, surface, and underwater are increasing. For the autonomous operation of the robot systems, the techniques including localization, environment recognition, path planning and following, formation control, and wireless communication among the robot systems are essentially required. A vast number of related studies are conducted to enhance their autonomy. The studies on autonomous navigation techniques for the robot systems as well as applying the robots to the various applications including exploration, monitoring, and harmful organism removal have been progressed. Also, since the late 1900s, jellyfish have increased largely in numbers and have become a problem worldwide. It has caused huge damages to marine-related industries. To solve the issue, this thesis introduces the development of a novel robot system for jellyfish removal. The robot system is designed based on a twin-hull-type unmanned surface vehicle (USV), includes a jellyfish removal device attached underneath the USV. The navigation system consists of an electrical control system and a GNC (guidance, navigation, and control) system and provides a strategy for jellyfish removal. The GNC system calculates the location of the robot, plans and follows a path to perform a task. Also, a vision-based jellyfish detection algorithm is introduced. The performance of the navigation and jellyfish detection, and the jellyfish removal was verified by the field tests in the ocean environment. The swarm robot system has many advantages compared to a single robot system in terms of performing a task such as exploration or inspection. For the efficient coverage operation of the swarm robot system, the formation control is an essential technique to follow a path while maintaining robots’ relative positions in their group. In this study, I propose a coordinated path following technique incorporating guidance algorithms into leader-follower framework. Each follower robot follows both its desired position for the formation control and the speed and heading angle of the leader robot by utilizing the guidance algorithms, while preventing collision between any two follower robots. The performance tests were conducted in the ocean environments with USV-based the swarm robot systems. Also, I studied the receding horizon (RH) formation control including collision avoidance. The RH control generates control inputs by predicting the future states of a system based on model information of the system. The formation control problem is defined by a nonlinear system according to RH scheme, and the problem is solved by particle swarm optimization (PSO). Sequential Monte Carlo (SMC)-based particle repair algorithm is proposed to guarantee asymptomatic stability of the control system. Also, a collision avoidance method is proposed to prevent collision among robots. To alleviate the influence of ocean current, the ocean current estimator is designed. This study is verified through numerical simulations.