Solving Operational Mazes Using Mono Sensor Platform Robot (Exprimental Research)

Yahya Hassanzadeh-Nazarabadi Platform Department Parse Lab of Robotics

Mashhad , Iran ya_ha_na@ieee.org

Amirali Mafi Robotic Research Center

Ferdowsi University Mashhad , Iran

amirali.mafi@yahoo.com

Sajjad Gharib Navaz Platform Department Parse Lab of Robotics

Mashhad , Iran sajjad.gharib@yahoo.com

Seied Milad Mohammadi Robotic Research Center

Ferdowsi University Mashhad , Iran

milad.aragon@yahoo.com

Rouhollah Mohammadzadeh Robotic Research Center

Ferdowsi University Mashhad , Iran

rouhollah.mohammadzadeh@gmail.com

Amin Alizadeh Platform Department Parse Lab of Robotics

Mashhad , Iran aminalizadeh_73@yahoo.com

Abstract—micro-mouse robots used to solve artificial mazes are unable to develop for solving operational mazes due to their weakness in drawing a perfect imagination from environment. In this experimental research this problem has been analyzed and solved by using mono sensor platform robot. This platform is able to gather information from environment with high accuracy and make a perfect picture that can be used to solve operational mazes with real conditions.

Maze; micro-mouse robot; ultrasonic sensor; stepper motor; platform (key words)

I. INTRODUCTION Micro-mouse robots are intelligent robots that can take

an unknown path to a certain goal without human intervention.

The unknown path experimentally is a maze, but it could be operationally a city or an unknown place. A maze consist of nesting complex paths that robot should inter from a corner as start point and reach the center (goal).

A micro-mouse robot consist of below parts : • Robot Body • Power Circuits • Movement • Sensors and Steering • CPU and Programming

So far, many algorithms are written for solving a maze by means of a micro-mouse robot. This algorithms allow the

researchers to select the best hardware to suit desired algorithm. Algorithms to solve a maze by a micro-mouse robot are:

• Following the walls • Checking available paths • Turn Counting • Metering • Image Processing

Many algorithms were written to solve a maze with micro-mouse robots, but based on the researches done on the micro-mouse robots and also the made-samples , these robots couldn’t be operational. Here operational is to using the robots in the real environments or at least mixed-reality. However the scale of micro-mouse robots is appropriate to operate in this environments. But because of missing the artificial condition (such as unequal distance between the walls) accuracy and performance of this robots significantly decrease. Because of competition theme of this robot , seldom researches were done on them. In this paper a Mono Sensor Platform Robot (MSPR) is used to introduce an optimized hardware consist of optimized processing, sensors arrangement, movement pattern. This intelligent system will be able to solve operational mazes in real environments. This system has been implemented experimentally and “following the walls” algorithm has been programmed on. The accuracy of this system to solve maze in the operational environments is significantly high, in comparison with micro-mouse robots.

2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks

978-0-7695-4821-0/12 $26.00 © 2012 IEEE

DOI 10.1109/CICSyN.2012.16

27

2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks

978-0-7695-4821-0/12 $26.00 © 2012 IEEE

DOI 10.1109/CICSyN.2012.16

27

Also the cost and accuracy for implementation of this system is less expensive than micro-mouse robots. First in section 2 we are going to introduce the problem. Then in section 3 we will discuss about the micro-mouse robots are made until today. In section 3 MSPR will be introduced. In section 4 we are going to prepare MSPR for implementing solving maze algorithms and in section 5 we will discuss about the accuracy of solving maze with MSPR in comparison with micro-mouse robots. Finally in section 6 we will conclude this experimental research.

II. PLANNING ISSUE Robot will work in an operational maze. An operational

maze is a complex of nested ways (Figure 1) . Ways are separated from each other with walls. For ease the maze is considered as a square. Width of paths isn’t same in the whole maze. It can vary from the robot dimensions to it’s sensors range. Maze has a start point in one of it’s corner and a stop point as a goal in it’s center. Robot should begin it’s operation from start-point and reach the goal without making any collision with the walls.

Figure 1- Operational Maze

III. PERVIOUS RESEARCHES So far , a large number of researches have been done on solving maze, specially by means of micro-mouse robots. This researches are all focusing on the algorithms of this robots. Thus the algorithms have made a good progress. According to the studies on the made-samples and also the simulations, can be claimed that this algorithms can solve maze with high accuracy and proper time order. unfortunately This algorithms couldn’t run on the micro-mouse robots in real environments as accurate as their simulations in artificial places , because of similar structure and following a general same theme in hardware design and sensors arrangement. Based on performed researches, the main reason of this problem is sensors inability to gather necessary information from environment. Hardware operates weak in designing a perfect imagination from circumference. This fact causes algorithm to make wrong decision and it’s accuracy and performance can be greatly reduced.

In this research a statistical society of micro-mouse robots with 47 robots was studied. All robots in this society have at least one championship. The society was broke into two famous themes. Figure 2 and figure 3 shows the samples of that themes. In these themes, sensors will be configured in the points that collecting information from is needed. For example robot in the figure 2 is able to collect information along right drop-out lines from it’s sensors. It’s so clear that this robot is unable to collect information from it’s side or back. The imagination of environment that hardware of this robot is able to draw is restricted to 4 broken lines and only covers a limited part of environment. Here is where the lack of information happens. Robot shown in figure 3 has the same disadvantage. In addition, because of using only one sensor, the imagination of environment will be more limited. The other robots in the society have the same disadvantage. It leads them to be optimize only for mazes with specific circumstance and to lack general aspect.

Figure 2- The most popular theme

Figure 3-The second popular theme

IV. MONO SENSOR PLATFORM ROBOT (MSPR) The main theme of a Mono Sensor Platform Robot (MSPR) consist of a ultrasonic sensor located on a stepper motor. Although MSPRs have many difference in implementation, their structures and mechanism are same. A simple but efficient sample of MSPR shown in figure 4

2828

and 5. This sample and all other samples of MSPR are made by the authors of this paper.

Figure 4-MSPR

Figure 5-Main Theme of MSPR

With a complete rotate of 360 degree bye stepper motor, ultrasonic sensor scans the 360 degree around a robot with a specific range. This information save in an one-dimension array of pixels. In this way, MSPR can draw a continuous picture and perfect imagination from environment during it’s motion without lack of information. This platform has basic algorithms for searching, scanning, mapping, sweeping. As a platform many algorithms can be run on MSPR without any change in details. One of these algorithm is solving maze. It could perform this algorithm either in artificial or operational environments. Before going to discuss about solving maze with MSPR, some important technical keywords of MSPR are defined here: Distance: Ultrasonic sensor sends an ultrasonic wave with a certain frequency. This wave will collide with an obstacle in its way and reflect toward the sensor. The sensor processor compute the distance between sensor and obstacle according to the time difference between go and return times.

D = V.T/2 (1) In the equation number 1 , “D” is distance between obstacle and sensor. “V” is the velocity of ultrasound in air and “T” is the time between one go and return of ultrasound wave. Resolution: Is the number of points that MSPR can sample in one 360 degree rotation. The minimum resolution of MSPR can define by programming, but the maximum resolution define by following equation:

R = 360 / degree (2) In equation number 2, “R” is the resolution of robot and “degree” is the minimum degree that stepper motor can rotate. Volume of data required for the algorithm, defines the best amount of resolution. A very high resolution will help MSPR to draw an accurate imagination from environment, but reduces the time performance of algorithm.

V. SOLVING MAZE WITH MSPR MSPR as a platform doesn’t need hardware changes such as changing the type of sensors or it’s amount or arrangements , changing the pattern of collecting information and so on for performing various algorithms. The solving maze algorithms can be run on MSPR easily and without any changes. All the algorithms that were stated in the introduction for solving maze, except for image processing, are easily applicable on MSPR. If we make a change in the structure of MSPR and use a camera instead of ultrasonic sensor, the image processing algorithm can be run on MSPR. But because the goal of this research is the platform aspect of MSPR and to avoid making any changes, image processing algorithm is ignored. For running various solving maze algorithms on MSPR, it just necessary to change the subprograms and functions of collecting information. After set a proper resolution for sampling from environment, we should set steps for sampling from that. Each steps define as the distance between two points. MSPR stops at that points and scans all around it’s 360 degree and then takes another step. Similar to resolution more number of steps (less distance between them) will results in accurate imagination of environment , but also reduced the timing performance of algorithm. The number of steps for a certain operation depends on the following fact: MSPR will sample in the end of each step. Therefore each step could be model as a circle. Center of circle is the end of step and it’s radius is the maximum range of ultrasonic sensor and defines as the visibility of robot in that point. As shown in figure 6 , for a certain operation, whatever number of steps are greater, the circles are intersecting in more places. Algorithm can correct information in intersection point and therefore the ability of algorithm to correct information will increase but in this case the total time of algorithm will increase. If we want to save time, as shown in figure 7, we could choose the number of steps so that the circles tangent to each other. Weakness of this case is that each point will be scanned exactly once and there will be no

2929

possibility of correction. If we want to significantly decrease the time of operation we could choose the steps so that the circles separate from each other. In this case we have lack of information per step. This case is not recommended.

Figure 6-Short steps of sampling

Figure 7-Proper steps of sampling

VI. RESULT OF EXPERIMENTS To experiment in an operational space, a maze similar to maze shown in figure 1 was built with the size of 8*8 meters and path widths vary from 200 to 400 millimeters. This sizes are about twice larger than the standard of competitions but very similar to operational space. Walls made of cardboard boxes with different size (high). The two teams with high experience in the field of micro-mouse asked to test their robot in this maze. The pictures of robots are shown in figure 2 and 3. In this paper we named that robots A and B respectively. Robot “A” uses “following the walls” and robot B uses “turn counting” algorithm. In these experiments the robot arrival time from start point to goal point and number collisions with the wall was investigated. Also because drawing the correct image of the environment was considered, number of dead-end path that was traveled by the robot considered as the robot accuracy. In each experiment, for standardization, MSPR speed was adjusted equal to the speed of other robot (either A or B) by means of PWM. At first Robot “A” and MSPR were compared. The “following the walls” algorithm was programmed on MSPR. Each robot was tested 10 times. The average of records are shown in table 1. Next these experiments were repeated between robot “B” and MSPR by programming “turn counting” algorithm on MSPR and the results are shown in table 2. As a result from table 1 and 2, in performing “following the walls” and “turn counting” algorithm, MSPR performs more accurate and significantly makes less faults. But due to time-consuming sampling, spends a considerable time. However, based on the operational nature of these tests, high precision and less fault can be largely offset MSPR time-consuming. Also MSPR is cheaper in comparison with other robots.

Cost Accuracy Time(s) Fouls Robot

500$ 56.07 % 73.13 11.41 A

100$ 81.49% 102.04 0.20 MSPR Table 1. Comparing MSPR and robot "A"

Cost Accuracy Time(s) Fouls Robot

300$ 67.13 % 51.02 6.00 B

100$ 78.81% 89.44 1.30 MSPR Table 2 . Comparing MSPR and robot "B"

It should be noted that in all tests carried out, the robots weight, engine torque and the power consumption are

3030

different. In all the experiments MSPR steps was chosen so that steps circle created was tangent.

VII. CONCLUSION In this experimental research , first we analyzed micro-mouse robots operation in artificial maze and their inability to solve operational mazes. Researches done before was reviewed as a statistical community. Statistical community strengths and weaknesses were analyzed. Next we introduced mono sensor platform robot (MSPR) and it’s ability to gather information with high accuracy and perform different kinds of algorithms such as solving maze. We discussed about preparing MSPR for solving maze. Very slight changes in some functions done and MSPR got ready to solve maze. Two famous and strength samples with different algorithms , as famous themes, were selected . These samples experimentally compared with MSPR to solve an operational maze with real circumstance. This result was obtained that MSPR is more accurate but slower than micro-mouse robots. Due to operational nature of this research low speed can be offset by high accuracy.

REFERENCES

[1] Y. Hassanzadeh-Nazarabadi, A. Firoozeh, and A. Mafi, “Mono Sensor Platform Robot” Proc. Electrotechnical Conference (MELECON), 16th IEEE Mediterranean ,Mar 2012, 10.1109/MELCON.2012.6196541.

[2] D.Hongshe ; S.Jinguo; G.Qin “An Efficient Algorithm for Robot

Maze-Solving” Proc. Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2nd International Conference ,2010, 10.1109/IHMSC.2010.119.

[3] M.Sharma; K.Robeonics, “Algorithms for Micro-mouse” Proc.

Future Computer and Communication, ICFCC 2009. International Conference on ,2009 , 10.1109/ICFCC.2009.38.

[4] S.Mishra; P.Bande, “Maze Solving Algorithms for Micro Mouse”

Proc. Signal Image Technology and Internet Based Systems. SITIS '08. IEEE International Conference on, 2008, 10.1109/SITIS.2008.104.

[5] X.Jiang; D.Xin; G.Wu; X.Jiang, “The Platform Design that Simulates

the Micromouse Move in the Maze” Proc. Intelligent System Design and Engineering Application (ISDEA), Second International Conference on,2012, 10.1109/ISdea.2012.441.

3131