This paper presents an innovative approach to semantic path planning for mobile robots by integrating semantic data from building digital twins. Semantic and metric information extracted from the digital twin is used to assign weights to a connectivity graph, allowing for path computation using the A* algorithm. Our method excels at generating robot-specific maps that combine both geometric and semantic data, diverging from traditional static maps. This semantic integration equips robots with diverse navigation skills, enabling them to navigate complex environments within large smart facilities. A key innovation of this work is our path planner, which utilizes semantic data from Building Information Modeling (BIM) databases. This marks a significant advancement in mobile robotic navigation, accommodating robots with varying navigation abilities. The significance of this work lies in the seamless integration of semantic data, enhancing the adaptability and efficiency of mobile robots, regardless of their navigation skills. This coordinated navigation system not only improves safety but also optimizes shared space management for both humans and robots.
We propose a deep learning based measurement model for Monte Carlo self-localization to improve the robustness to robot calibration errors in the RoboCup Humanoid League context. A deep neural network estimates the distance between a line mask computed by the vision pipeline from a camera image and given pose candidates. This distance is then used to derive weights for the particles of the Monte Carlo localization. The network is trained using images generated by applying a perspective transformation on the map of the field. We use a style transfer model to preprocess the input data for finetuning the model to build robustness towards noise and detection errors in real-world data. Our model is more robust to erroneous head pose measurements than the baseline. Further, we show significant improvements in global localization.
In 2024, B-Human won the Standard Platform League Champions Cup and the Shared Autonomy Challenge at the RoboCup world championship as well as the RoboCup German Open. This paper provides an overview of some of our new approaches that we believe have contributed most to improving our already very successful code base. This ranges from adaptions to recent rule changes over multiple new vision and motion approaches to the implementation of this year’s technical challenge.
We present a lightweight keypoint-based Recurrent Neural Network (RNN) approach to the 2023 RoboCup Standard Platform League Visual Referee Challenge. The goal of the challenge is to classify 13 different static and dynamic referee gestures during a robotic soccer match. The developed solution must be lightweight and perform well from anywhere on the field. Multiple referees may be present on the field, with the distinguishing trait of the visual challenge referee being their red gloves. We use the lightweight Convolutional Neural Network pose detector BlazePose to extract the pose key-points. To isolate the referee in the frame, HSV colour segmentation is used to find red glove regions and mask unneeded sections of the image. A lightweight RNN is used to classify the sequence of keypoints. We compare the results of our work to previous solutions developed for the 2022 Visual Referee Challenge. Analysis on real-time testing shows our method achieves strong performance for all gestures on many different locations on the soccer field.
In this paper, we present our approach that earned first place at the RoboCup@Work 2024 competition. We briefly outline the current competition challenges and give an update on our system architecture and methods used for planning, manipulation, perception and navigation. The emphasis is on newly implemented features and relevant changes compared to the design of prior years. Finally, we evaluate the results of the 2024 RoboCup@Work including the additional technical coworker challenge. A performance comparison with other teams is provided and further conclusions are drawn by revisiting the challenges and goals outlined last year.
