AUTMan Humanoid Teen Size Team Description Paper RoboCup 2017 Humanoid Robot League, Nagoya, Japan

Sepehr Ramezani, Amirali Setaieshi, Nima Pourmohammadi, Parsa Yarahmadi, Armin Arvand, Foruzan Fallah, Amirhossein Hosseinmemar, Jamillo Santos, Kyle Morris, Meng Cheng Lau, John Anderson, Jacky Baltes, Sourosh Sadeghnejad

Bio-Inspired System Design Lab (BInSDeLa), Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Ave., Tehran, Iran, P. O. Box 15875-4413; Autonomous Agents Laboratory, University of Manitoba, Winnipeg, Canada, R3T 2N2

http://autman.aut.ac.ir · http://aalab.cs.umanitoba.ca

Abstract This document introduces AUTMan (Amirkabir University of Technology and University of Manitoba) humanoid joint team for participating in Humanoid Teen Size Robot League in RoboCup 2017, Nagoya, Japan. This joint team was founded in 2013 on collaboration between two teams from Amirkabir University of Technology (Tehran Polytechnic), Iran and University of Manitoba, Canada. A brief history of Team AUTMan and its' research interests and directions for future research will be described. Our main research interests within the scope of humanoid robots are to modify our recent TeenSize platform, ARASH, for being compatible to long-term road map for the future of the league, active balancing, robot manipulations, accurate localization, and strategic reasoning for soccer.

1 Introduction

From the beginning of ambitious new road-map of the humanoid league, the RoboCup Federation encouraged universities and research institutes toward making some joint research groups [1].

To reach the RoboCup final goal, the AUTMan humanoid robot team from the Bioinspired system design laboratory of Amirkabir University of Technology, Iran started a joint collaboration with team Snobots from the Autonomous Agents Laboratory at the University of Manitoba Canada. Both groups have a long history in humanoid robot soccer and were quite successful in recent years. Besides winning various Technical Challenges in both the KidSize and TeenSize sub leagues in previous RoboCups, 2013, 2014, 2015 and 2016, the joint team won the main RoboCup humanoid robot league tournaments. Placing 2nd in RoboCup 2013 in the humanoid Kidsize and 3rd in RoboCup 2015 and 2016 humanoid in the teen sized sub league proved our wide and planned endeavors in promoting the humanoid robotics communities.

On the other hand, AUTMan was quite active in the scientific and research events, where they published some journal and conference papers in different RoboCup symposia [2-5] and humanoid robotics related conferences and journals [6-8].

To improve our collaboration in 2017, we have focused on modifying our humanoid TeenSize platform to become lighter, which can easily be used by other research groups. In the 2017 competition, we want to display the advancements made in our research in the fields of active balancing, robot running, designing a modular hands for robot manipulations, modifying our ROS-based software for accurate localization, strategic reasoning for soccer and also improving the robot vision and detection algorithms. Therefore, we continue the collaboration of our team for the RoboCup humanoid kidsize league 2017 and extend our future activities toward this goal.

2 Development for 2017

In 2017, the goal of our collaboration and our participation in RoboCup 2017, are three-fold.

• 1. We will focus on modifying our recent humanoid TeenSize robot platform, ARASH, based on the humanoid league long-term road map.
• 2. We are working on improving the localization, behaviors, team play, and collaborations between robots.
• 3. We extend our collaboration to this sub league and continue to demonstrate the feasibility of joint teams in RoboCup competition and symposium.

3 Hardware Design

We will use a modified version of our previous year developed robot, ARASH an Anthropomorphic Robot Augmented with Sense of Human (ARASH).

It is a robust robot with 100cm in height and 7.5Kg in weight which we focused on weight reduction and also nimble reaction features. We designed the robot based on the anthropomorphic data, collected from different human. ARASH has 20 degrees of freedom (DOF) in total. As explained in detail in [9], to reduce the risk of robot performance and to increase the robot wide range of motion and fast reaction and to reach to a higher performance, we used two Dynamixel MX-106 servo series in the knee joints.

We have two levels of control hardware in ARASH as well as our KidSize Platform KIARASH. For the higher level, we use a MAXData QutePC-3001 [10] mini embedded board as the main pc. The lower level motor control and the walking engine are implemented on a self-designed modular micro-controller board. The communication protocol and the block diagram of the electronic subsystem are shown in Figure 2. The processor used in the main board is a STM32F756NG, a high-performance processor and DSP core with FPU. The processor core is based on the ARM Cortex-M7 32-bit RISC core operating at up to 216 MHz frequency. It is used for main and walk processor [4]. This year, we designed additional hardware to provide richer human robot interaction by having special embedded boards for speech recognition and gesture generation.

For 2017, we focus on the need for a light weight running robot which is necessary for promotion of the RoboCup humanoid league. For achieving to this purpose, we started a new field of study in our research of humanoid robotics for having a compliant actuators. A running humanoid robot's research is focused on the interface between motor and loads, which being imposed while the contact of the robot's feet to the ground. So, you need to have actuator as stiff as possible. In the growing fields of humanoid robotics, in which, nowadays, researches are devoted their most of the time to improve the robots' running and jumping abilities, the variable stiffness actuators or adjustable compliant actuators are being designed and implemented because of their good shock tolerance, lower reflected inertia, more accurate and stable force control and the potential for energy storage capabilities.

4 Software Development

The following subsections describe the software architecture and our improvements for RoboCup 2017, Nagoya, Japan.

4.1 Software Architecture

To develop an architecture for our software system, we will use the ROS [11] so that our work can be more easily shared with other researchers and RoboCup teams.

4.2 Walk Engine

A block diagram of our Omni-directional walk algorithm is illustrated in Figure 3. This year, to speed up the computation of our inverse kinematics, we implemented a look-up table for the computation of the atan2 function. In the C/C++ programming language functional implementation of the atan2 function use a BBP (Bailey-Borwein-Plouffe) type formula [11].

4.3 Push Recovery

To begin with, this year for push recovery challenge, we use full upper body push recovery method. For this purpose we designed a new upper body for our TeenSized robot which will add more degrees of freedom for the robot and made it 25 degrees of freedom in total. It has an articulated spine with five actuators. The spine not only allows ARASH to move similar to human, but helps to balance the robot by adjusting its posture.

4.4 Cognition

Our cognition module processes input in several layers to detect objects of interest. In object perception, we find objects in the field, by their shapes and in object recognition, we recognize what these shapes actually are.

Color classification: We process wide-angle YUV422 images from a Logitech C920. We construct a lookup table by selecting random pixels based on our desired color labels. This Lookup table is a mapping from YUV color space to a set of colors and assigns a class label to every pixel. For example, we use this algorithm to recognize green color and find the borders of the playing field. After color classification, we scan the image vertically to create rectangles of the same color.

Vertical Scan and Segmentation: The main purpose of the segmentation layer is to find an approximate coordination of objects in the image by clipping color labeled regions of interest (ROIs). The ROIs, at first, are rectangles made by a vertical scan line algorithm, to improve the performance of this algorithm a convex hull of the field is computed and items outside of this hull will be ignored. Next the adjacent segments are merged and make shapes. The identity of the resulting shape is estimated by heuristics which either a circle (the ball) or a line (the field lines and the goal posts) this strategy significantly improves the performance since the color labeling is not necessary anymore.

Edge based algorithm: our color-based algorithms are not able on their own object to detect various objects. So we convert our image into edge based segments. Actually, we extract edge pixels from the image, based on the spot's edge magnitude and gradient. Since it is not efficient to apply edge detection to the entire image, we applied our edge detector on the coordinates of shapes, which we found in previous steps.

4.5 Robot Behavior

This year we use a separate processor for the behavior process. We have a method implementing a path planer that takes 3 arguments: distance of the ball from the current position of the robot, distance of the ball to the goal, and distance of the ball from the nearest object in the field. After lining up behind the ball we consider two options. If an object blocks the direct path from the current position of the ball to the goal, we use an A* basedl_path_planner to dribble the ball to the goal. If there is a clear path from the current ball position to the goal, then we kick the ball at the goal. We identify the goal posts and if an object covers the goal area, we aim for the side that is less obstructed.

4.6 Remote Robot Control

Formerly, all the connections with the robots have been established using a simple SSH connection and all the logging and simulation processes were forced to be run on the robot while the robots are on the field. This is simple to implement but puts a heavy processing load on the main computer. So all these process are moved to the remote pc and the robots only handles the network connection while running. With this approach the remote control of the robot is more robust.

We also developed new GUI applications to help in the control, debugging, and visualization of the robot.

4.7 Localization

We use different data to increase the accuracy of our localization algorithm. Vision data, odometer algorithm and positioning based on accelerometers of our IMU are some data that we use to have better localization. In positioning we get signals from the IMU which represents acceleration of the robot in 3 dimensions. If the acceleration of an object is known, we can obtain the position data by integrating the acceleration twice. The input signal is not noise free so it must be mechanically and digitally filtered to improve accuracy.

We have odometer data in our walk and we use it for filtering of our vision data and after that we use a particle filter and positioning by IMU to estimate our position on the field.

5 Conclusion and Acknowledgment

This report described the technical plans and works done by the AUTMan Humanoid TeenSize Robot Joint Team for its entry in RoboCup 2017. Our main research interests within the scope of humanoid robots are to modify our recent TeenSize platform, ARASH, to be compatible with the long-term road map of the humanoid league. We also improved our active balancing, accurate localization, and strategic reasoning for soccer. Also, we are trying to start to have a step toward having a running robot by making the compliant actuators. For further information, please refer to our publications.

References

1. Gerndt, R., Seifert, D., Baltes, J.H., Sadeghnejad, S., Behnke, S.: Humanoid Robots in Soccer: Robots Versus Humans in RoboCup 2050. Robotics & Automation Magazine, IEEE 22, 147-154 (2015)
2. Baltes, J., Sadeghnejad, S., Seifert, D., Behnke, S.: RoboCup Humanoid League Rule Developments 2002–2014 and Future Perspectives. RoboCup 2014: Robot World Cup XVIII, pp. 649-660. Springer (2015)
3. Shangari, T.A., Shamshirdar, F., Azari, B., Heydari, M., Sadeghnejad, S., Baltes, J.: Real-time Ball Detection and Following Based on a Hybrid Vision System with Application to Robot Soccer Field.
4. Shangari, T.A., Shamshirdar, F., Heydari, M.H., Sadeghnejad, S., Baltes, J., Bahrami, M.: AUT-UofM Humanoid TeenSize Joint Team; A New Step Toward 2050's Humanoid League Long Term RoadMap. Robot Intelligence Technology and Applications 3, pp. 483-494. Springer (2015)
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7. Gerndt, R., Seifert, D., Baltes, J.H., Sadeghnejad, S., Behnke, S.: Humanoid robots in soccer: Robots versus humans in RoboCup 2050. Robotics & Automation Magazine, IEEE 22, 147-154 (2015)
8. Baltes, J., Bagot, J., Sadeghnejad, S., Anderson, J., Hsu, C.-H.: Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees. KI-Künstliche Intelligenz 30, 245-255 (2016)
9. Sadeghnejad, S., Baltes, J., Ramezani, S., Karimi, M., Karimi, M., Valaei, A., Javadi, M., Ahmadi, A., Hosseinmemar, A., Santos, J.: AUT-UofM Humanoid TeenSize Team Description Paper.
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