This repository is used for integrating the main controller on the robot Qolo [1], all sensors, obstacle avoidance, and high-level controllers with the low-level motor control of the robot [3].
Package contents:
User Iterface:
- Embodided control with Hands-free Navigation [2]
- Joystick remote control with html interface
Controllers:
- Redirecting Driver Support (RDS): Obstacle Avoidance for non-holonomic robots [P1]
- Modulated Dynamical Systems based obstacle avoidance [P2]
- Compliant control [P3]
Communication:
- ROS controllers for real-robot Qolo.
- ROS controllers version for the CrowdBot simulator version of Qolo. [P4]
Mantainer: Dr. Diego Paez G. Start Date: 2019-01-01
Last Update: 2021-02-15
Ubnutu 16.04 or 18.04 ROS kinetic / melodic python 2.7 python-pip rds_network_ros # For shared control --> See related package below
jsk_rviz_plugin * sudo apt-get install -y ros-VERSION-jsk-visualization rwth_messages (for people tracker visualization -- See below in Visualization section)
Install Ubuntu Kernel for Upboard: https://wiki.up-community.org/Ubuntu enablel spi port on UP board https://wiki.up-community.org/Pinout_UP2#SPI_Ports
Enable the HAT functionality from userspace https://wiki.up-community.org/Ubuntu
Install MRAA library: https://github.com/intel-iot-devkit/mraa PYYAML: For FT model
Follow the installation guide: https://github.com/DrDiegoPaez/qolo_ros/blob/master/install_scripts/Install_instructions.txt
Qolo overall system guide:
https://github.com/DrDiegoPaez/qolo_ros/blob/master/Guides/Qolo_LASA_Specs.pdf
Launch sequence guide for real Qolo:
Remote Joystick setup and usage:
General Usage guide for CrowdBot Simulator:
Install the packages: {also found online}
visualisation/messages.zip visualisation/spencer_tracking_rviz_plugin.zip
- Only compatible with ROS-kinetic or ROS-melodic (Ubuntu 16 or 18)
catkin_make of the ros package on your wrokspace source your workspace roslaunch qolo_ros rviz.launch
You can select among different presets for rviz depending on the available data (LIDARs / sensory board / force sensors / people tracker)
[P1] Obstacle avoidance for tight shape and non-holonomic constraints (used in shared control): [3]
https://github.com/epfl-lasa/rds
[P2] Obstacle avoidance based on dynamical systems:
https://github.com/epfl-lasa/qolo_modulation https://github.com/epfl-lasa/dynamic_obstacle_avoidance/
[P3] Pybullet simulation for collisions and compliant control with Qolo and other robots [4]:
https://github.com/epfl-lasa/human-robot-collider
[P4] CrowdBot Simulator Package:
https://gitlab.inria.fr/CrowdBot/CrowdBotUnity
[P5] Qolo 3D model for simulation files:
https://github.com/FabienGrzeskowiakInria/CrowdBot_robots
_Qolo Design _(please cite the latest T-Mech paper):
[1] Paez-Granados, D., Kadone, H., Hassan, M., Chen, Y., & Suzuki, K. (2022). Personal Mobility with Synchronous Trunk-Knee Passive Exoskeleton: Optimizing Human-Robot Energy Transfer. IEEE/ASME Transactions on Mechatronics, (1), 1–12. https://doi.org/10.1109/TMECH.2021.3135453
[1] Paez-Granados, D., Kadone, H., & Suzuki, K. (2018). Unpowered Lower-Body Exoskeleton with Torso Lifting Mechanism for Supporting Sit-to-Stand Transitions. IEEE International Conference on Intelligent Robots and Systems, 2755–2761. https://doi.org/10.1109/IROS.2018.8594199
Qolo Hands-free control:
[2] Chen, Y., Paez-Granados, D., Kadone, H., & Suzuki, K. (2020). Control Interface for Hands-free Navigation of Standing Mobility Vehicles based on Upper-Body Natural Movements. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2020). https://doi.org/10.1109/IROS45743.2020.9340875
Qolo shared control:
[3] Gonon, D. J., Paez-Granados, D., & Billard, A. (2021). Reactive Navigation in Crowds for Non-holonomic Robots with Convex Bounding Shape. IEEE Robotics and Automation Letters, 6(3), 4728–4735. https://doi.org/10.1109/LRA.2021.3068660
[4] Paez-Granados, D., Billard, A., & Suzuki, K. (2020). Materializing Personal Standing Mobility from Design to Shared Control. In ETH Zurich Rehabilitation Engineering Lab (Ed.), CYBATHLON Symposium. Cybathlon. https://cybathlon-symposium.ethz.ch
Crowd Navigation & safety assessment:
[5] Paez-granados, D., Gonon, D., Salvini, P., & Billard, A. (2020). Physical Safety in Collisions Between Robots and Pedestrians. IEEEE International Conference on Robot and Human Interaactive Communication (ROMAN-2020) Workshop on Robots from Pathways to Crowds, 1–2. https://doi.org/10.13140/RG.2.2.28087.55209
[6] Grzeskowiak, F., Gonon, D., Dugas, D., Paez-granados, D., Chung, J. J., Nieto, J., Siegwart, R., Billard, A., Babel, M., & Pettr, J. (2021). Crowd against the machine : A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd. IEEE International Conference on Robotics and Automation (ICRA-2021).
Compliant Control:
[7] Paez-Granados, D., Gupta, V., & Billard, A. (2021). Unfreezing Social Navigation : Dynamical Systems based Compliance for Contact Control in Robot Navigation. Robotics Science and Systems (RSS) - Workshop on Social Robot Navigation, 1(1), 1–4.http://infoscience.epfl.ch/record/287442?&ln=en. https://youtu.be/y7D-YeJ0mmg
Contact: [Dr. Diego Paez] https://diegofpaez.wordpress.com/
Acknowledgments This project was partially founded by:
The EU Horizon 2020 Project CROWDBOT (Grant No. 779942): http://crowdbot.eu
The Toyota Mobility Foundation (TMF) through the Grant: Mobility Unlimited Challenge 2019: https://mobilityunlimited.org
Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of (MEXT) Japan: http://www.ai.iit.tsukuba.ac.jp/research/046.html