Research Dataset Catalog and ShareScore Ratings | ScienceDex

zenodo40/100

A dataset for robotic outdoor visual navigation with multiple passages through trajectory segments

The images were captured by a fisheye camera and a magnetic compass was used to acquire the orientation data. The datasets are split in two folders: 1) LEARN: In order to learn a new place, the robot camera captures 15 images over a 360 degrees panorama. During this process, the robot stays still in order to avoid distortions in the representation of the place. 2) EXPLO: When exploring the environment (i.e. the rest of the time), the robot only captures 7 images per panorama, for the purpose of faster place recognition. Images are captured while the robot is moving. Various exploration panoramas are recorded around the trajectory performed in the learning panoramas (see traj.pdf). The average distance between two learning panoramas is 0.93 +/- 0.03 meters The average distance traveled during an exploration panoramas is 0.71 +/- 0.01 meters DATASET A --------- - 20 meters long - 22 learning panoramas (i.e. sets of 15 images captured while robot is stopped) - 5 exploration trajectories - A_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving) - A_parallel: 29 exploration panoramas - A_diagonal1: 28 exploration panoramas - A_diagonal2: 30 exploration panoramas - A_diagonal3: 29 exploration panoramas DATASET B --------- - 20 meters long - 21 learning panoramas (i.e. sets of 15 images captured while robot is stopped) - 4 exploration trajectories - B_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving) - B_parallel: 29 exploration panoramas - B_diagonal1: 29 exploration panoramas - B_diagonal2: 29 exploration panoramas DATASET C --------- - 23.1 meters long - 25 learning panoramas (i.e. sets of 15 images captured while robot is stopped) - 2 exploration trajectories - C_on_learned: 34 exploration panoramas (i.e. sets of 7 images captured while robot is moving) - C_parallel: 34 exploration panoramas PANO_INFO FILE STRUCTURE ------------------------ Every folder containing images also contains an info file, named either learn_pano_info.SAVE or explo_pano_info.SAVE. Each line corresponds to an image. The structures is the following: - column 1: id = image_id + 1 - column 2: azimuth of the center of the image in degrees/360 (value in [0,1]) - column 3: elevation of the center of the image. irrelevant in this database (equal to 0). - column 4: type of panorama: equal to 1 if learning and to 0 if exploration. - column 5: end of panorama: equal to 1 if it corresponds to the last image of a panorama. REFERENCES ---------- The dataset was used in the paper: Belkaid, M., Cuperlier, N., and Gaussier, P. Combining local and global visual information in context-based neurorobotic navigation. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 4947-4954, doi: 10.1109/IJCNN.2016.7727851, 2016.

opencc-by-4.0Dec 2016View details →

zenodo40/100

Robust and Adaptive Robot Self-Assembly Based on Vascular Morphogenesis

Self-assembly is the aggregation of simple parts into complex patterns as frequently observed in nature. Following this inspiration, creating programmable systems of self-assembly that achieve similar complexity and robustness with robots is challenging. As role model we pick the growth of natural plants that adapts to environmental conditions and is robust to disturbances, such as changes due to dynamic environments and cut parts. We program a robot swarm to self-assemble into tree-like shapes and to efficiently adapt to the environment. Our approach is inspired by the vascular morphogenesis of plants, that is the patterned formation of vascular tissue to transport fluids and nutrients internally. The aggregated robots establish an internal network of resource sharing, allowing them to make rational decisions collectively about where to add and where to remove robots. As an effect, the growth is adaptive to an environmental feature (here, light) and robust to changes in a dynamic environment. The robot swarm is able to self-repair by regrowing lost parts. We successfully validate and benchmark our approach in a number of robot swarm experiments showing adaptivity, robustness, and self-repair.

opencc-by-4.0Feb 2018View details →

zenodo40/100

BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 25: The fifth obstacle with 100 robots after passing all robots

The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance.