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SICK Robot Day 2014

delivery of wooden cubes

This year autonomous robots were delivering wooden cubes (a printed barcode defined the target location). Up to four robots were competing at the same time. There were 15 teams competing. How it went? See the compilation of the best teams … Update: 21/10 — Photos (Standa)



This article is a compilation of contributions from several participating teams. The content may evolve with the time, as new texts will arrive or some parts will be translated cs<->en. Please note, that currently the Czech and English version are very different! The Czech version (Google translation … it looks like Google is giving up in the middle) primarily contains blog about Eduro Team preparation, so it is much longer.

Content

Note, that all other teams did not finish with a positive score.

1st place: PARMA Team, Parma

PARMA (Personal Autonomous Robot for Mobile Applications) Team

International competition for mobile robot. The challenge is organized by SICK AG and RIMLab took part in 2010 and 2012, and won the latter. This year robotic race is a kind of mailman race. The arena is a convex bullpen of about 13 x 13 meters, which contains an "island" in the middle with the filling stations. At each round four robots receive a wooden cube with a bar code from a filling station. The bar code gives the number of the goal stations to reach. The more goal stations are correctly visited in the given time (10 minutes), the higher is the team score. Of course, the robots must avoid collision with other robots and the border of the arena. For more information see Robot Day general rule. The event took place on Saturday October 11th 2014 in the Stadthalle in Waldkirch (Germany). PARMA (Personal Autonomous Robot for Mobile Applications) from the University of Parma won the competition delivering 5 cubes in the first run and 6 cubes in the second one.

First Run

Second Run

Robot Hardware: Actuators and Sensors

The robot is a MobileRobots Pioneer 3DX provided with a basket to carry the wooden cubes as required by the competition. The basket has the shape of a funnel to bring the cubes to its center.
The basket contains two kinds of sensors to read the barcodes placed on the faces of the cubes: a Sick ICR803-B and two Logitech C270 USB cameras. Sensor ICR803 is positioned to keep the proper distance from the center of the cube (about 10 cm). The USB cameras provide a redundant cube detection system based on a computer vision library to read barcodes (zbar library). A LED is placed inside the basket to light up the cube and a green one to signal that the robot is ready to receive or deliver. The LEDs and the interface with Sick ICR803-B are controlled by an Arduino One board.
The robot is also equipped with a Sick LMS100 laser scanner and a Point Grey Bumblebee XB3 part of Vislab 3DV Stereo Vision. The LMS100 laser scanner is the reliable and robust sensor that allows obstacle detection and navigation. The 3DV stereo vision system provides the left and right images, the disparity map and the 3D point cloud obtained by stereo processing. Unfortunately, there was not enough time to develop algorithms for 3D processing (e.g. to estimate the 3D poses of numbered plates).

Computer Vision Perception

The computer vision is used to detect numbered plates and targets. Number plates are the labels of goal stations. The targets are placed in front of each filling and goal station. Like for many computer vision methods, the developed algorithm initially search the regions of interest (ROI) and, then, performs classification and tracking. First, the ROIs are found by extracting lines (using EDLines algorithm) and rectangles from each frame. Second, in the case of plates, the content of the ROI is classified by a trained Artificial Neural Network (ANN) according to the OCR histogram of the rectified ROI. In the case of targets, the detector searches circles, lines and their intersection points.
The pose of plates and targets is estimated using the projective geometry and the information on their size. Furthermore, the estimated poses (i.e. the projection in the scan plane) are validated by the laser scanner. A pose is valid only if its projection corresponds to an obstacle.

Navigation

The navigation module manages the mission state, the path planning and the robot motion. It consists of several subtasks.
  • Local Planner. The local planner builds a local occupancy grid map using the measurements from the laser scanner and inflates the obstacles. Given the goal pose (e.g. a pose in front of a numbered plate or of a filling station), the planner computes a trajectory on the occupancy grid map according to a graph search algorithm (tested algorithms: Generalized Voronoi Disgram, A*, D*).
  • SLAM Module. The SLAM module builds a map of the environment consisting of the robot poses and of the numbered plates used as landmarks, and iteratively updates the estimation of robot pose w.r.t. the initial reference frame. The odometry and vision measurements are integrated into the Graph SLAM framework g2o. This modules provides the goals (the filling and goal stations) to the planner.
  • Approach Task. The approach module allows the robot to reach its goal (a filling or goal station) aligned with the arena fence. It exploits the observation of the targets in the image.
The following video illustrates how the modules work together with a simulation (the simulator is Gazebo, integrated in ROS).

PARMA team, winner of Sick Robot Day 2014!

Front from left to right: Fabrizio Castelli (Sick Italia), Fracesco Valenti, Marco Pedretti, Marco Paini. Back from left ro right: Cecilia Bollati (Sick Italia), Marco Allodi, Fabjan Kallasi, Domenico Giaquinto, Dario Lodi Rizzini.
Thanks to Sick Italia, CUS Parma and Vislab for their support.

Questions & Answers

Where there any failures of your robot during competition?

The main failures we observed are related to the recognition of filling station state. Actually, the robot checked whether the filling station is occupied by another robot by assuming that the filling station fence appear like a segment when observed by the laser scanner. Furthermore, such segment must lie close to the expected position of the filling station (from the map). In the end of second run, it seems that free filling station detection is difficult. We suppose that two things can have influenced such behavior.
1) We expected a square island in the middle of the arena (the rule of procedure is somehow ambiguous: They are located at the 4 sides of a square island (en-rules) and instead it was octagonal. Of course, we adapted thresholds, but the initial assumption has an impact on the algorithm design.
2) The pose of filling stations is not updated by the mapping module, but it is initially set. The map contains only robot and numbered plates poses (with the constraints given by odometry measurements). Hence, an error in robot pose estimation (that, in spite of SLAM, increases a bit) can affect the decision on busy/free state.

Any other surprises?

Another surprise was the limited number of goal stations: only 4, we expected 8. This low number increased traffic and competition for stations. This was a problem at least in our first run.

How did you detect the navigation target?

There are slightly different algorithms to detect numbered plates and targets.
  • Numbered plates: detection relies on rectangle detection and, then, the inside is classified by a trained ANN on histogram.
  • Targets: detection exploits circle detection (at different scales, to make it more robust).
The pose of the two target is estimated through perspective geometry (mono-camera estimation, not stereo!) since the size of the two items is known (see solvePnP() in OpenCV). In both cases, the laser validation is required. The numbered plates are detected by vision also from 12 m if no occlusion occurs, but since laser validation is required to accept it the full detection occurs when the robot is closer to the plate. Of course, the estimation only with vision is noisy in particular for the orientation of the plates (the yaw angle changed also of 20 deg between two observations!). Thus, a proper tracking was added.
We had a problem (never observed in our month-long trials in Parma!) during the trials in the morning. One of the windows with a sloopy roof was similar to plate with number 4 and its size was compatible with the distance of the filling station fence. We added a constrain: to be accepted a number must lie on the external fence. This solved the problem and, then, they closed the curtains in the afternoon.

2nd place: Attempto Tübingen

This year we were able to continue our successful participation at the SICK robot day, placing second out of 14 teams from Germany, the Czech Republic, England and Italy. The competition was again organized by SICK AG in Waldkirch near Freiburg, Germany.

The task

This year's task was to collect cubes at a collection station at the center of a circular arena. These cubes were labeled with barcodes, which represented one of four delivery stations at the outer border. These deliveries were marked with the same signs already in use back in 2010. The robot got one point for every correctly delivered cube, and one negative point for each wrong delivery.
Our robot was able to deliver 6 cubes in 10 minutes in the first round, just as many as the winning team PARMA delivered in their better run. In the second run we had a hardware problem, leading to a 5 minute pause in which our robot did not move at all. Afterwards the robot was able to collect a few cubes, but not enough to achieve the first place.

Our approach

Our robots Arnie and Sly are based on a former, omnidirectional RoboCup robot that has attended the SICK robot day twice in the past. They were heavily revised in order to meet the requirements for the tasks of this year's event. The most important revision was to make the robots as low as possible to be able to drive under the pick up and delivery rings.
A combination of multiple methods was implemented to solve the sign and target detection problem. These includes artificial neuronal network based machine learning methods as well as Hough line detection. Furthermore we implemented a RANSAC based map analysis to detect the central area, which was supposed to be square, but ended up being an octagon.
We used a customized version of slam_karto for localization and mapping, which was optimized for faster grid map generation. Furthermore we employed laser scan segmenation to only map long segments for a more robust system. Path planning was done using A* with an omnidirectional motion model and path following was implemented using orthogonal projection and an exponential law.

Team Attempto

Attempto Tübingen is a team of students and faculty staff with background in Computer Science, Bioinformatics, Automation, Control and Cognitive Science. Our two robots Arnie and Sly are based on former RoboCup models that have attended SICK Robot Day twice.

Team Members

More information

Questions & Answers

Failures?

  • Waiting times were too long. We waited the full 10 seconds at delivery and pick up stations. We had to wait for ~6 seconds every time we were approaching a target sign.
  • In the second run we had a spurious measurement of our main laser scanner which reported an obstacle directly in front of our robot, which caused a 5 minute long pause.

Surprises?

  • The rules stated that the central area was square, so we used this knowledge to find the four pick-up stations. On site we had to rewrite this central part of our system to detect an octagon instead of a square and to decide, which four sides of the octagon were equipped with a station and which not.

How did you detect navigation targets (Zielscheibe)?

First we projected the front laser scan onto the image to mask out everything but the border plane in which the targets where positionend. Further masking was performed using morphological gradients to detect regions of interest.
Then we performed Canny line detection and the Hough Transform on these ROIs to find vertical and horizontal lines and looked for the crossing point, the circles / ellipses were totally ignored. We then used the camera calibration matrices in combination with the projected laser scan to determine the pose of the target. The poses were averaged over a few measurements, which was not that efficient (especially on our Core2Duo CPU) and caused the waiting pauses.

4th place: Eduro Team, Praha

Robot Eduro participated on SICK Robot Day already in 2010 and 2012. This was an advantage, because we already know how difficult the contest is and we had some idea what troubles we can expect .

Robot

Eduro is already proven prototype platform with proprietary SMAC (Stepper Motor — Adaptive Control) motors, CAN bus, a single board x86-based computer running Linux OS (AMD Geode CPU/500MHz, 256 MB RAM, compact flash card, Wi-Fi, 3 Ethernet, 1 RS232 and 2 USB ports) and it is powered by two 12V/8Ah sealed lead acid batteries.
Eduro is equipped with CCTV IP camera with with fish-eye lens. Since 2010 it has also laser scanner LMS100, thanks to SICK. You can find more about the platform in the Robotour 2010 proceedings.
There was an extra bar code reader attached to USB port for this contest.

Software

The software is written mostly in Python and some parts, like image processing, are written in C/C++ (using OpenCV library). The source code is freely available at:

Version 0

I think that concept of „version 0” can be dated back to Eurobot contest where robots had to pass homologation i.e. score at least one point without opponent robot. It is the simplest thing that could possibly work, i.e. eXtreme programming in robotics. If you have ver0 you can relax a little bit — you have something working. You can also start to collect data and see what bits are the real problem and where improvement is needed. And if everything goes wrong you still have something to compete with .
For SICK Robot Day 2014 this meant delivery of one cube. Well, 24 hours before the contest we did not have tested algorithm what could be called version 0

Target recognition

The main difference (when compared to 2010 contest) were navigation targets and missing columns with numbers. This algorithm was critical because without that Eduro failed to collect a cube.
The recognition of numbers (see bellow) is based on contours extracted from thresholded image. It was necessary to use the same concept to reduce computational requirements. The idea was to look for a contour containing twelve smaller contours. Because black lines are relatively thin it was necessary to erode image by 3x3 kernel (see Python version recognizeNavTarget() for details).
We had to add some constrains for these 12 small contours to limit the number of false detections. There were surprises like for example the area of the bigger arc does not have to be bigger then area of inner arcs (it should be mathematically but sometimes it fails in reality). Minimal bounding rectangles were used instead and „area” was then estimated from their size. And that worked fine.
The rejection rule was that 4 biggest sub-contours have to be in the four corners and then next 4 biggest sub-contours have to be again in all four corners. This was even fun to watch: see video — frames with detected target are 10 times slower and once a while you can see „random colors” which are 1+12 contours groups failing to fulfill the criteria.

Digits recognition

The recognition of digits did not work very well in 2010 — in particular I still see the image of rObOt day poster where two letters 'O' were connected and Eduro classified that as digit '8'. The old algorithm expected first to detect the frame and then only decided what is the content, but … there were really bad light conditions in 2010 and plate frames just „disappeared”.
The 2014 code was very similar to the one in 2010. Note, that there was also an experimental solution on parallel RaspberryPi which I refused to integrate at the very end as „not sufficiently tested”. On the way to Germany (approx. 12 hours drive due to many traffic jams) this code was revised so that it could work even without the frames. We needed high reliability of detection so we limited the size of numbers to really big, the height and position in the image had to be in given proportions, and we re-introduced frame detection. Image erosion needed by target recognition also helped with frame detection.

Results

The contest for us was really the fight for the 3rd place (and the winners of 2010 and 2012 would split the first and second place). Eduro expected stright wall near the navigation target, and that sometimes failed for used octagon.
Because Eduro is not omni-directional robot, and image frames were approx. at 1Hz with 1s delay the idea was to get 40cm from the wall with laser feedback and turn towards the target (processed camera). Eduro has very precise odometry so it is capable to turn by desired angle and backup.
While during the tests at home we sometimes had problem detect target even at 50cm in Waldkirch it worked from 2 meters! Pickup worked in 4 of 4 attempts, but the problems were near the goal.
The first problem was that there was an obsolete code using angles instead of absolute position for approaching numbers from close distance.
The second major problem was missing verification if robot is heading towards island or border causing infinite shaking in order to recognize non existing cube.
Thanks to „blocking rule” we were allowed to restart Eduro and collect two cubes in each run. In the second run we re-entered the same feeder and got minus point for that. In total we finished on shared 4th place which I would still consider a success .

Questions & Answers

How did you solve the non-square center problem?

Well, we did not. The pickup of the first cube was without any problem and for the next cubes we tried to navigate to the center of the „occupied area in the middle”. There was a backup to go around the island if the target was not recognized and that would work for both octagonal as well as square. Verification that nearest reading is from perpendicular obstacle was not added at the end.

Photo (Stanislav Petrásek)



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