Team Description Paper

Sebastian Bejos, Fernanda Beltran, Ivan Feliciano, Giovanni Guerrero, Moroni Silverio1∗

Abstract

We describe the design of the hardware andsoftware components, as well as the algorithmsthat the robots of the Laboratory of Algorithms forRobotics(LAR) solve for the problem of the Stan-dard Educational Kit(SEK) category of the LatinAmerican Robotics Competition(LARC) 2014.

1. INTRODUCTION

The team of the Laboratory of Algorithmsfor Robotics (LAR) is made up of students fromthe School of Higher Studies (FES) Acatlan, ofthe National Autonomous University of Mexico(UNAM). LAR is a research group of the UNAMdedicated primarily to the design of combinato-rial and geometric algorithms with applications inplanning the movements of mobile robots. On thisoccasion it will be the third year that LAR teamparticipates in the Latin American Robotics Com-petition, highlighting that in their first participation(in 2011), they obtained third place. In addition,in April of this year we participated in the Mexi-can Robotics Tournament (TMR) with the rules ofIEEE 2015, and won first place; LAR teams pre-viously won two first places in the years 2011 and2014, and a third place in 2013.

First we will describe the design of the robotsand explain their parts and functionality, then wewill give the strategies and the algorithm being im-plemented to solve the problem. We will concludewith the different behaviours that the robots couldtake.

∗1Laboratorio de Algoritmo para la Robotica, Centrode Desarrollo Tecnologico, Facultad de Estudios SuperioresAcatlan UNAM

2. DESIGN

According to the specifications and task de-manded by the “CLOCLON”; we designed andbuilt two robots to work independently one in eachmodule, the first ”Mr. Agile” to act on the wallsmodule and the second ”Miss Plow” whose taskwas in the cave module.

In order to build the robots, 2 LEGO MIND-STORMS Education EV3 Core Sets were used,one for each robot, as well as omnidirectionalwheels and servomotors from the HiTechnicLEGO MINDSTORMS NXT kit. To program ourrobots we used the ROBOTC programming lan-guage, which is based on the C programming lan-guage.

2.1. Miss Plow’s Design

The first robot, named ”Miss Plow”, was spe-cially designed to maneuver in the cave modulebecause being the largest and having no obstaclessuch as the walls module allows for easy mobil-ity of a large robot. As the name says the aim ofthis robot is to “sweep” everything in its path, try-ing to bring as many humanoids as possible in theentrance module (rescue ship). Its role is justifiedby obtaining the most points, no matter how manywrong humanoids it carries. ”Miss plow” has twomain components, the car, which is the mechanismthat makes it to move, and the structure surround-ing it, which is where the humanoids acumulate tobe pushed to the entrance module.

On top of the robot is the brick and at the sameheight, in the front and in the rear, are two ul-trasonic sensors to help verify the distance to thewalls before entering into the module. Lower thanthe previous ultrasonic sensor is a gyro sensor thathelps turn the robot as accurate as possible. On top

of the brick is an element made of small Lego kitpieces to add weight to the robot, and keep it fromlifting the wheels off the floor. At the bottom aretwo servomotors from the Lego NXT kit, whichare responsible for moving the two front wheels ofthe robot. Since our goal was to bring as manyhumanoids this involved a lot of weight that mustbe moved by the robot, to solve this, we neededmore torque. Therefore the servomotors controlleda gear of 12 teeth and it was connected to a largergear of 36 teeth; the latter moved the wheel.

Figure 1. A Lower Gear produce a larger outputforce and a larger mechanical advantage.

2.2. Mister Agile’s Design

This is a smaller robot, allowing it to reducethe collisions with walls placed in the walls mod-ule. It has three wheels, the two front tires are eachsubject to a separate servomotor, this makes it eas-ier to do turns; the rear wheel is an omnidirectionalHiTechnic wheel.

Mister Agile has a rotation sensor for preciseturns and to reduce the deviation error when it ad-vances forward. To detect the humanoids it has acolor sensor just below the claw (which will be ex-plained later). The color sensor allows the robotto detect the humanoid at all times once it is cap-tured, this because at random occasions claw mayclose without capturing a humanoid, preventingthe robot go to the module without rescuing a hu-manoid. The claw is closed once a humanoid isseen by the color sensor, as the robot reaches itsdestination with the humanoid then it opens andgoes back to leave the humanoid and the robot con-

tinues to rescue another humanoid.Mr. Agile has two ultrasonic sensors, one in

the front and one in the back, both are locatedhigher than the humanoids maximum height; theobjective of these sensors is to prevent the colli-sion of the robot with the walls. It also adjusts itsdistance from the walls, so it can keep track of howstraight it is advancing after some time. The robotcan collide with the walls intentionally to positionitself in a straight position. It can only transporta humanoid at once and can successfully rescue ahumanoid every 2 minutes.

Figure 2. Mister Agileand Miss Plow: Top View

3. STRATEGIES AND PROBLEM SOLU-TION

As previously mentioned, our strategy was todivide the work for the limited time that was estab-lished in the rules. In the subsequent sections weexplain the tactics for the solution of the challenge.

3.1. Mister Agile’s Strategies

The strategy for Mr. Agile is complicated be-cause it enters a module where the number of wallsranges from one to four walls, and they can alsobe in different configurations specified in the rules.The route of the robot is performed once we knowwhich configuration of the walls is going to be usedin the competition. The program which generatesthe code for the robot route allows us to make al-ternate travel routes for the robot with minimal ef-

fort. We used an average to know approximatelyhow much a servomotor had to turn for the robotto move forward 10 cm (which is our unit). Thisrobot classifies humanoids, if a humanoid is of thecolor it needs to save it takes the humanoid to thecorresponding module, if it is not, then it takes it tothe entrance of the corresponding module so MissPlow brings that humanoid when it arrives with theothers and we can obtain more points. After ad-vancing k units, the robot makes an adjustment us-ing the data from the gyro sensor, this to preventit from going as straight as possible, because ofthe sensor not being very precise. When the robotis near a wall it leans against it so you it can re-turn to a straight position, if it tries to straightenitself with the help of a wall and it hits a humanoidthen it tries again until it is straight. When it ob-tains a straightened position the robot considers itas the new reference position. The robot knowswhere the walls are because matrix data structureis built and every time the robot advances its coor-dinates are changed (position and orientation withrespect to the matrix). According to the coordi-nates it decides whether it makes an adjustmentwith the walls or not. When the robot finds ahumanoid it leaves the walls module through theshortest route, no matter where it is. To carry thisout we did a program to make searches in ampli-tude (for the shortest route) on all possible wallconfigurations, this simple robot program allowedus to go and leave a humanoid and return to whereit was (when it found the humanoid) following theshortest route. The robot based on its position andorientation followed the matrix data structure filledwith arrows (created by breadth-first search) thatindicated the route the robot must follow to exitthe walls module.

3.2. Ms Plow’s Strategies

The strategy for Miss Plow is simple, to havea planned trajectory to take as many humanoids asit can. The figure shows the route the robot travels.The width of the robot is a little less than 3 squares,so is the lenght. Therefore we seek to make theroute cover as most ground as possible in the mod-ule without entering the cave. Because it doesn’t

Figure 3. Matrix that shows the shortest scapeway for a some walls configuration.

Figure 4. Program that generates the rut insidethe walls module (the “8” represent the walls)

classify the humanoids it gets, his priority is to fol-low the route exactly as planned.

Because we don’t actually know the exactnumber of humanoids that could be in the cavemodule, neither how they would be distributed inthis module, we chose to divide the path of therobot in two parts. In the figure, the red and blackfor the right route and the left route, respectively.The blue and yellow arrows show the route to go tothe entrance of the cave module and the sip, whenwe are in the central module and assuming that the”ship” of the robot is in that position.

As it was described previously, we wanted thatMiss Plow be very precise in its progress and turns.The same strategy as Mister Agile was used toincrease the accuracy of progress, encoders weremeasured to establish a unit of progress of approx-imately 10 cm.

The value of the rotation sensor has an errordue to noise. The way we devised to prevent theaccumulation of that error that affected the per-formance of the robot along its established pathwas restarting its value crashing at strategic pointsmarked in the figure. The method to prevent therobot from following a straight line as planned wasthat at distances greater than a preset, the route wasdivided into intervals such that we checked that thevalue of the gyro sensor was not more than a pre-defined error established from the beginning.

As we mentioned in the design, to ensure thatthe robot always go out or enter the cave mod-ule the ultrasonic sensors were used in case theprogress and degrees of the rotation sensor madethe robot get stuck. The figure shows light graysquares where the robot performed readings fromthe front, back or both ultrasonic sensors accordingto the location of the robot.

4. CONCLUSIONS

Through the algorithm described above, ourrobots were able to meet the objective of compe-tition, maybe not entirely, since we believe that thetime allocated for each round of the competitionwas not enough. Overall they were able to rescuethe largest number of humanoids, generating themost points.

Figure 5. Robot’s path scheme for a CLOCLONwith modules of the same dimensions. (1.90 mx 1.90m) )

ACKNOWLEDGMENTS

The authors of this document would like tothank Fernando Gonzalez and Araceli Perez fromthe Center for Technological Development. Alsoto the Applied Mathematics and Computation De-partment for all the support given to us at differentphases of this project.

References

[1] Rules of SEK Category – 2015/2016. 14st IEEE LatinAmerican Robotics, Rules of SEK 2015 Category.

[2] Dave Astolfo, Mario Ferrari, Giulio Ferrari, BuildingRobots with LEGO Mindstorms NXT. Syngress Publish-ing, 2007.

[3] Daniele Benedettelli, Creating Cool Mindstorms NXTRobots. Apress, 2008.

[4] John C. Hansen, Lego Mindstorms NXT Power Pro-gramming Second Edition. Variant Press, 2009.