E i n d h o v e n

Recente Publicaties

  • Houtman, W. (TU/e), Kengen, C.M. (TU/e), van Lith, P.H.E.M. (TU/e), ten Berge, R.H.J. (TU/e), Kon, J.J. (TU/e), Meessen, K.J. (TU/e), ... van de Molengraft, R. (TU/e) (2019). Tech United Middle Size League Winner 2019. In RoboCup 2019: Robot World Cup XX (pp.517-528). (Lecture Notes in Computer Science; Volume 11531 LNAI, 2019). Springer. Tech Tech United Eindhoven Middle-Size League Winner 2019. Abstract - After the sequence of winning the RoboCup Middle-Size League (MSL) in even years only (2012, 2014, 2016, 2018), Tech United Eindhoven achieved its first RoboCup win during an odd year at RoboCup 2019. This paper presents an evaluation of the tournament and describes the most notable scientific improvements made in preparation of the tournament. These developments consist of our solution to (unforeseen) localisation problems and the improvements in the control architecture of our eight-wheeled robot. The progress in the shooting lever is elaborated as well as the advancements in the arbitrary ball-detection in order to improve our scoring during the Technical Challenge. Additionally, research towards the application of artificial intelligence in predicting the actions of opponents and recognizing the appearance of the opponent robots will be presented.
  • Van Lith, P. (TU/e), van de Molengraft, M.J.G. (TU/e), Dubbelman, G. (TU/e), Plantinga, M. (TU/e) (2019). A Minimalistic Approach to Identify and Localize Robots in RoboCup MSL Soccer Competitions in Real-time.  Minimalist MSL Robot Location 5.0 Abstract -This work provides a real-time Convolutional Neural Network to infer the team identity and location of soccer robots in the RoboCup Midsize League. It has been designed to have a fast turn-around time of less than 30 minutes between collecting robot images and an operational neural network, allowing the network to be trained in the time available between matches. This is an important feature, as the identification markers are unknown before a competition. This Fully Convolutional Network uses Global Average Pooling to generate a Class Activation Map with information about the location of every robot. A Blob detector is used to find the locations of the robots of each team which are translated into real-world coordinates, to determine the best game strategy.
    The resulting network allows for inference rates of more than 60 Hz.
  • Kon, J. (TU/e), Houtman, W. (TU/e), Kuijpers, W. (TU/e), & van de Molengraft, R. (TU/e) (2018). Pose and Velocity Estimation for Soccer Robots. Student Undergraduate Research E-Journal!, 4. Abstract - This paper details the design and real-time implementation of a planar state estimator for soccer robots. A camera system, encoders, gyroscope and accelerometer are combined in a two-stage Kalman filter through a constant acceleration model. Inflating Noise Variance is employed to handle slip and ensure convergence in stationary periods. The approach oers substantial improvement w.r.t. the old pose estimator.
  • de Koning L. (TU/e), Mendoza J.P. (CMU), Veloso M. (CMU), van de Molengraft R. (TU/e) (2018) Skills, Tactics and Plays for Distributed Multi-robot Control in Adversarial Environments. In: Akiyama H., Obst O., Sammut C., Tonidandel F. (eds) RoboCup 2017: Robot World Cup XXI. RoboCup 2017. Lecture Notes in Computer Science, vol 11175. Springer, Cham. Abstract - This work presents a pioneering collaboration between two robot soccer teams from different RoboCup leagues, the Small Size League (SSL) and the Middle Size League (MSL). In the SSL, research is focused on fast-paced and advanced team play for a centrally-controlled multirobot team. MSL, on the other hand, focuses on controlling a distributed multi-robot team. The goal of cooperation between these two leagues is to apply teamwork techniques from the SSL, which have been researched and improved for years, in the MSL. In particular, the Skills Tactics and Plays (STP) team coordination architecture, developed for centralized multi-robot team, is studied and integrated into the distributed team in order to improve the level of team play. The STP architecture enables more sophisticated team play in the MSL team by providing a framework for team strategy adaptation as a function of the state of the game. Voting-based approaches are proposed to overcome the challenge of adapting the STP architecture to a distributed system. Empirical evaluation of STP in the MSL team shows a significant improvement in offensive game play when distinguishing several offensive game states and applying appropriate offensive plays.
  • Schoenmakers, F. (TU/e), Meessen, K. (TU/e), Douven, Y. (TU/e), van de Loo, H. (TU/e), Bruijnen, D. (TU/e), Aangenent, W. (TU/e), ... van de Molengraft, R. (TU/e) (2017). Tech United Eindhoven Middle size league winner 2016. In RoboCup 2016: Robot World Cup XX (pp. 542-553). (Lecture Notes in Computer Science; Vol. 9776 LNAI). Springer. Abstract - The Tech United Eindhoven Mid-size league (MSL) team won the 2016 Championship in Leipzig. This paper describes the main progress we made in 2016 which enabled this success. Recent progress in software includes improved perception methods using combined omnivision of different robots and integrating the Kinect v2 camera onto the robots. To improve the efficiency of shots at the opponents’ goal, the obstacle detection is improved. During the tournament new defensive strategies were developed as an answer to the advanced attacking strategies that were seen during the round robins. Several statistics of matches during the tournament show the overall performance of Tech United at RoboCup 2016.
  • Kuijpers, W. (TU/e), Neves, A. J. R. (TU/e), & van de Molengraft, R. (TU/e) (2017). Cooperative sensing for 3D ball positioning in the RoboCup middle size league. In RoboCup 2016: Robot World Cup XX (pp. 268-278). (Lecture Notes in Computer Science; Vol. 9776 LNAI). Springer. Abstract - As soccer in the RoboCup Middle Size League (MSL) starts resembling human soccer more and more, the time the ball is airborne increases. Robots equipped with a single catadioptric vision system will generally not be able to accurately observe depth due to limited resolution. Most teams, therefore, resort to projecting the ball on the field. Within the MSL several methods have already been explored to determine the 3D ball position, e.g., adding a high-resolution perspective camera or adding a Kinect sensor. This paper presents a new method which combines the omnivision camera data from multiple robots through triangulation. Three main challenges have been identified in designing this method: Inaccurate projections, Communication delay and Limited amount of data. An algorithm, considering these main challenges, has been implemented and tested. Performance tests with a non-moving ball (static situation) and two robots show an accuracy of 0.13 m for airborne balls. A dynamic test shows that a ball kicked by a robot could be tracked from the moment of the kick, if enough measurements have been received from two peer robots before the ball exceeds the height of the robots.