hi_dynamic_control

An open source bipedal robot control framework for Hi robot with serial ankle joint, based on nonlinear MPC and WBC.

Installation

Install dependencies

• OCS2

• ROS1-Noetic

Install OCS2

OCS2 is a huge monorepo; DO NOT try to compile the whole repo. You only need to compile ocs2_legged_robot_ros and its dependencies by following the steps below.

You are supposed to clone the OCS2, pinocchio, and hpp-fcl as described in the documentation of OCS2.

# Clone OCS2
git clone https://github.com/leggedrobotics/ocs2.git
# Clone pinocchio
git clone --recurse-submodules https://github.com/leggedrobotics/pinocchio.git
# Clone hpp-fcl
git clone --recurse-submodules https://github.com/leggedrobotics/hpp-fcl.git
# Clone ocs2_robotic_assets
git clone https://github.com/leggedrobotics/ocs2_robotic_assets.git
# Install dependencies
sudo apt install liburdfdom-dev liboctomap-dev libassimp-dev

Compile the ocs2_legged_robot_ros package with catkin toolsinstead of catkin_make. It will take you about ten minutes.

catkin config -DCMAKE_BUILD_TYPE=RelWithDebInfo 
catkin build ocs2_legged_robot_ros ocs2_self_collision_visualization

Clone and Build

# Clone
mkdir -p <catkin_ws_name>/src
cd <catkin_ws_name>/src
git clone https://github.com/HighTorque-Robotics/hi_dynamic_control.git

# Clone SDK
git clone https://github.com/HighTorque-Robotics/livelybot_robot.git

# Install dependencies
sudo apt install libserialport0 libserialport-dev libreadline-dev

# Build
cd <catkin_ws_name>
catkin init
catkin config -DCMAKE_BUILD_TYPE=RelWithDebInfo

# for different use build 
# gazebo simulation 
catkin build pi_controllers pi_description pi_gazebo

# real robot deploy 
catkin build pi_controllers pi_description pi_bridge_hw

#  Robot hardware 
catkin build pi* yesense* livelybot*

Gazebo Simulation

Run the gazebo simulation and load the controller:

roslaunch pi_controllers one_start_gazebo.launch    

Notes: After the user starts the simulation, the robot will fall down in Gazebo. Firstly the user needs to set kp_position=60, kd_position=1 with rqt and reset the simulation by pressing Ctrl+Shift+R to reset robot.

Real Robot Deploy

Check port permissions of IMU devices and motor devices.

ls /dev/tty*

There will be five ACM devices, which are interfaces between imu and motors. Give them permissions so that the controller can read and write IO.

sudo chmod 777 /dev/tty*

Then you can launch the robot hardware.

roslaunch pi_controllers one_start_real.launch    

Gamepad Control

1. Press A button Reset State Estimator

2. push the left joystick once，then press LB to load controller.

3. Press RB to set walk (Unnecessary for Hi).

4. Use the joystick to publish command velocity.

Control Without Gamepad

1. reset_estimation

rostopic pub --once /reset_estimation std_msgs/Float32 "data: 0.0" 

2. load_controller

rostopic pub --once /load_controller std_msgs/Float32 "data: 0.0" 

3. publish a initial velocity with rqt gui tool

rosrun rqt_robot_steering rqt_robot_steering 

4. set_walk

rostopic pub --once /set_walk std_msgs/Float32 "data: 0.0" 

Project Reference

hunter_bipedal_control

legged_control

Paper Reference

# State Estimation

[1] Flayols T, Del Prete A, Wensing P, et al. Experimental evaluation of simple estimators for humanoid robots[C]//2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). IEEE, 2017: 889-895.

[2] Bloesch M, Hutter M, Hoepflinger M A, et al. State estimation for legged robots-consistent fusion of leg kinematics and IMU[J]. Robotics, 2013, 17: 17-24.

# MPC

[3] Di Carlo J, Wensing P M, Katz B, et al. Dynamic locomotion in the mit cheetah 3 through convex model-predictive control[C]//2018 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2018: 1-9.

[4] Grandia R, Jenelten F, Yang S, et al. Perceptive Locomotion Through Nonlinear Model-Predictive Control[J]. IEEE Transactions on Robotics, 2023.

[5] Sleiman J P, Farshidian F, Minniti M V, et al. A unified mpc framework for whole-body dynamic locomotion and manipulation[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 4688-4695.

# WBC

[6] Bellicoso C D, Gehring C, Hwangbo J, et al. Perception-less terrain adaptation through whole body control and hierarchical optimization[C]//2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). IEEE, 2016: 558-564.

[7] Kim D, Di Carlo J, Katz B, et al. Highly dynamic quadruped locomotion via whole-body impulse control and model predictive control[J]. arXiv preprint arXiv:1909.06586, 2019.