MRL Middle Size Team: RoboCup 2015 Team Description Paper

M.Gholipour, A.Karambakhsh, H.Rasam Fard, A.H.Maaroof Mashat, E.Saeedi Kamal, M.Farsi, F.Fathali Beyglou, M.Esmaeili Mollasaraei, M.AmirSardari, S.E.Marjani Bajestani, A.Abbasian Amiri, Sh.Arash, M.Yeganeh Doost, N.Montazeri, A.Abdolmaleki

Mechatronics Research Laboratory, Islamic Azad University of Qazvin - IRAN

Abstract This paper concisely describes the very main and new features of our soccer playing robots along with the improvements made since the previous years. Our major concerns for this year's competitions have been developing a new feed forward control system, designing a new Omni vision structure, designing a new ball handling system, also developing a new feature in AI software which is an accurate role assigner.

Key words: Localization; Control; Mapping; Navigation; Omni-directional vision

1 Introduction

The MRL middle size team has started its work at Mechatronic Research Laboratory of Azad University of Qazvin since Aug 2003. This team aims at establishing an intelligent control method for autonomous multi-robot systems in dynamic uncertain environment. MRL has begun the research and work in MSL since 2004. Our first official participation was during Robocup 2005 competitions in Osaka and then Robocup 2006 in Bremen. We optimized hardware, control and software system for Robocup 2008 and designed robust system for Robocup 2009 in Graz, as a result we find ourselves between four top teams. In Robocup Singapore 2010 competitions we got the first place of technical challenge and again fourth place of league competitions. Also, we got second place of free challenge and fifth place of league in Robocup Turkey 2011 competitions. After a year of hard work, we managed to get the second place of league in Robocup Mexico City 2012 competitions.

We believe that, the Intelligent, cooperative and adaptive behavior of the robots is very important factor for a team success. With this regard our research is continuously focused on: reliability, sensor fusion, dealing with uncertainty of environment for the robots, world modeling and dealing with missing information. In the following sections we briefly explain current status and new achievements of our team.

2 Hardware and mechanical features

We designed a 4-wheel omnidirectional robot which is equipped with MAXON EC 200 watt brushless motors which provides more speed and easy to control [1]. Main processor is Lenovo X200 notebook PC and electronic equipment are developed with ARM Cortex M3® -LPC1768 microcontrollers with high speed CAN-bus. This year we designed a new ball handling system and new Omni vision structure which is explained in the following sections.

Table 1. Hardware specification of the robot

2.1 New ball handling structure

This year, a new ball handling system has been developed as shown in figure 4 that enables the robot to control and hold the ball while it's driving in any direction even it's turning around. Also it would be able to catch the ball with higher factor of safety while passing or ball abduction by raising the height of wheels relative to the center of ball that allows us to use less energy and gain more speed or momentum.

2.2 Electrical Design

The new electronic system of MRL team has been designed and developed continuously for more than 2 years. Our system consists of power, Kicker, monitoring, FDS, E2C and DMS boards; this approach simplifies the repairing process especially during the matches. Figure 5 shows the diagram of our Electric part.

2.3.1 Power Board

This module has a responsibility to distribute the power for all subsystems and turning the systems on and off. Also many parts same as IR sensors spin-back's motors and monitoring LEDs are controlled in this module.

2.3.2 Kicker Board

The kicker board is designed to control the high voltage. It has one MOSFET for charging and four for kicking. An LPC2368 micro controller is used as controller. It creates pulse, limits the charger and communicates with the processor. The board also contains MOSFET driver to turn on and turn off the MOSFET in nanoseconds which prevents damaging them. The control board of the kicker circuit is a state machine with two states. The process begins by polling Up the Kick-Flag signal by main processor (Laptop) at state one. A signal that called Kick-flag is entered to the component to set the desired kick duration. When a high logic value is read by microcontroller at state two, the kicking sequence is initiated. In this state the microcontroller holds the kick signal high for the specified period of time. Keeping up signal (Kick-command), with high level logic at the different times can be creating different Kick powers. This process takes about 18 seconds. For improving the performance of this operation, we need to reduce this time to about 5 ~ 10 seconds

2.3.3 Ball detection sensor

For recognizing the ball position in dribbler and distance of the ball from the dribbler, two IR transmitters and receiver sensors were used. This module is useful when robot tries to get accurately behind the ball.

2.3.4 FDS

We have a fault finder and monitoring system that give the capability of observing the required data from different parts of the robot without any connecting to the robot, also continuously reporting the available information on the CAN network and save them on SMD card. This module would be developed for new extensions like real-time system check or some manual commands.

2.3.5 DMS

This new hardware has ability to illustrate the data rate in online format. This system shown the transfer as array LED where 50% means that system work properly, and if the data rate shown more that 60% continuously, it means that, fault happened in system that caused traffic in data transfer and if it was less than 40% continuously it means that, lost in transition happened.

3 Software (AI and High Level Control)

3.1 High Level Control

The high level control system receives predicted data from vision software and destinations from AI planner module and make robot go to its destinations. So, the important part of control system is a path planning algorithm and we used the VORONOI as path planning algorithm [2].

The control system will find the path (VORONOI) by using start position, end position, initial velocity along x and y axis and direction of starting point and ending point. Using the result path usage time will be calculated in order to generate the velocity command which will be sent to the robot. The velocity command is generated separately for each point along the trajectory according to the frame rate. Each frame has its own velocity command. The velocity profile is generated by using Bang-Bang algorithm and makes a robot trapezoidal velocity as shown in figure 6.

3.2 Software architecture

The software architecture for our decision making system consists of three parts: Plays, Roles and Skills. Skills are any single tasks that robots can do. For example "go to point" is a skill. Each player in a real soccer game has a role like defender, forward, goalie and etc. Compared with real soccer game each robot can change its role at the right time. Switching time is an important approach to manage robots, for this reason we need an accurate role assigner. Hence, we have developed a new role assigner module that assigns role to the robot according to game state and cost of roles for each robot

4 Vision system and localization

Our vision system hardware is composed of a UEye camera that stands upwards with a hyperboloid mirror above it. This component provides an omni-directional vision. The output of this system is very reliable and accurate.

To process the gathered images, at first a median filter is applied in order to reduce image noises, and then the four standard color marks will be assigned to each pixel by the Color Lookup Table. The Color Lookup Table (CLT) is filled in another program, which classifies the HSL Color Space into four standard colors. This program takes some supervised samples from user to learn how to recognize the standard colors. In run time this CLT is used in an image processing algorithm to detect the ball, field, and obstacle areas in the image in real-time (50 frame/s) on the laptop computer.

Self-localization is obtained through matching white lines in the camera pictures with the actual model. To recognize the lines in the pictures, we scan the radius of the picture from center shown as figure 8. Then categorize the white and green connected spots, we register the center of white spot groups, which next and previous lines are green, as a part of the line and keep on this process for all radiuses, so a group of spots from the field lines are recognized shown as figure 9. At last the spots are converted from polar to Cartesian coordinate and from pixel mode to metric mode by using mirror equations shown as figure 10. Then, the position is again calculated by matching the spots with actual model of field lines shown as figure 11.

To recognize ball, first, the ball colors are segmented, circular shape segment is recognizes as the ball with designed algorithm. But now we are able to recognize any standard FIFA ball.

This year we use stereo vision system by another camera in front of the goalie to calculate the height of the kicked ball and precision enhancement of recognizing the ball far away from it. Also we are going to improve the ball detection algorithm when a small part of the ball appears in the images; this is implemented by circle fitting algorithms [3].

5 References

References

1. V.Rostami, S.Ebrahimijam, P.Khajehpoor, P.Mirzaei, M. Yousefiazar "Cooperative Multi Agent Soccer Robot Team," International conference in enformatika system science and engineering, Volume 9, November 25-27, 2005, ISBN 975- 98458-8-1, page 95-98.
2. J.W. Choi, R.E. Curry, G.H. Elkaim, Real-time obstacle avoiding path planning for mobile robots, in: Proceedings of the AIAA Guidance, Navigation and Control Conference, AIAA GNC 2010, Toronto, Ontario, Canada, 2010.
3. Nicolaj C. Stache, Henrik Zimmer, "Robust Circle Fitting in Industrial Vision for Process Control of Laser Welding", Proceedings of the 11th International Student Conference on Electrical Engineering, Prag