Primitive robot kinematics and collision checking.
Project description
Acrobotics
Quickly test motion planning ideas is the goal, and Python seems like a great language for rapid prototyping. There are great libraries for robot simulation and related task, but installing them is can be a hassle and very dependent on operating system and python version. The drawback is that I have to write a lot of stuff myself. I'm not sure if it is useful to do this. But it will be fun and I will learn a bunch.
This library provides robot kinematics and collision checking for serial kinematic chains. The idea is that this library can be easily swapped by another one providing the same functionality.
The acro part comes from ACRO a robotics research group at KU Leuven in Belgium.
Installation
pip install acrobotics
Or for development
git clone https://github.com/JeroenDM/acrobotics.git
cd acrobotics
python setup.py develop
No Windows support for the moment because python-fcl is not supported. :( In the future I will possibly switch to pybullet. In the meantime, use windows subsystem for linux. MacOS is not tested yet.
Gettings started
(Code for example below: examples/getting_started.py)
This library has three main tricks.
Robot kinematics
T = robot.fk(joint_values)
IKSolution = robot.ik(T)
Forward kinematics are implemented in a generic RobotKinematics
class.
from acrobotics.robot_examples import Kuka
robot = Kuka()
joint_values = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
T_fk = robot.fk(joint_values)
Analytical inverse kinematics only for specific robots:
ik_solution = robot.ik(T_fk) # T_fk is a numpy 4x4 array
print(f"Inverse kinematics successful? {ik_solution.success}")
for q in ik_solution.solutions:
print(q)
Inverse kinematics successful? True
[ 0.1 -1.0949727 2.84159265 2.87778828 0.79803563 -1.99992985]
[ 0.1 -1.0949727 2.84159265 -0.26380438 -0.79803563 1.1416628 ]
[0.1 0.2 0.3 0.4 0.5 0.6]
[ 0.1 0.2 0.3 -2.74159265 -0.5 -2.54159265]
Collision checking
bool = robot.is_in_collision(joint_values, planning_scene)
First create a planning scene with obstacles the robot can collide with.
from acrobotics.geometry import Scene
from acrobotics.shapes import Box
table = Box(2, 2, 0.1)
T_table = translation(0, 0, -0.2)
obstacle = Box(0.2, 0.2, 1.5)
T_obs = translation(0, 0.5, 0.55)
scene = Scene([table, obstacle], [T_table, T_obs])
Then create a list of robot configurations for wich you want to check collision with the planning scene.
import numpy as np
q_start = np.array([0.5, 1.5, -0.3, 0, 0, 0])
q_goal = np.array([2.5, 1.5, 0.3, 0, 0, 0])
q_path = np.linspace(q_start, q_goal, 10)
And then you could do:
print([robot.is_in_collision(q, scene) for q in q_path])
[False, False, False, False, True, True, True, True, False, False]
Visualization
robot.plot(axes_handle, joint_values)
robot.animate_path(figure_handle, axes_handle, joint_path)
from acrobotics.util import get_default_axes3d
fig, ax = get_default_axes3d()
scene.plot(ax, c="green")
robot.animate_path(fig, ax, q_path)
More details
There's a more in depth explanation in the jupyter-notebooks in the examples folder.
And motion planning?
Comming soon.
Project details
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