|M.Sc Student||Refael Gilad|
|Subject||Design and Modeling of a Minimalistic Robotic|
|Department||Department of Autonomous Systems and Robotics||Supervisor||ASSOCIATE PROF. Amir Degani|
Over the past few decades, various strategies have been used to monitor the underwater environment for the purpose of military security or scientific underwater survey. Recently, the use of autonomous underwater vehicles (AUVs) has been implemented to avoid risk of human life in dangerous operations and allow longer and deeper missions. Most AUVs are large and expensive machines which include numerous actuators and sensing capabilities. In a different approach to the use of a single autonomous vehicle, there have been recent attempts to using a coordinated group of vehicles as a swarm of simple underwater robots. These swarms are able to perform complex tasks by using a large number of robots spread over the underwater environment. Due to the large number of agents in a robotic swarm, simple and cheap robots pose an advantage as they can be easily manufactured in abundant quantities.
In this research, we propose a simple and minimalistic mechanism which later could be used as an agent in a robotic swarm. The mechanism is capable of locomoting in a two dimensional environment using only a single motor and two passive flaps. The mechanism comprises two concentric bodies, with their centers connected by a servo motor. By inputting a symmetric oscillatory motion, the robot moves forward in a repetitive manner. We discovered that despite the robot having a single motor, by inserting an asymmetrical input to the motor, the robot performs a turning motion, i.e., its average orientation is changing. Furthermore, by inserting inputs with different asymmetries, the robot can turn in various radii. The two basic gaits, forward and rotational motions, along with a sensing system, would enable us to design a control system which will add a path tracking capability to the robot.
We developed a simplified mathematical model which captures the essence of the motion of the robot. We then used the model to simulate the motion of the robot and showed that the model captures the principle motion. We used the simulation to sweep through various design and input parameters and examine their effect on the performance of the robot.
We built a proof-of-concept mechanism to validate the model and simulation results. We also set up a test-bed which comprises a small swimming pool and a Vicon T10S motion capture system which enabled us to track reflective markers placed on the robot. We also used a MIRO M310 high-speed camera to study parts of the motion which were unseen on regular video. We used the experiments to try and validate the assumptions we made while developing the model. The experiments allowed us to validate the model and make a qualitative comparison between the simulation results and the experiment.