How to Control a Robot with Python

PyBullet is an open-source simulation platform created by Fb that’s designed for coaching bodily brokers (corresponding to robots) in a 3D setting. It supplies a physics engine for each inflexible and comfortable our bodies. It’s generally used for robotics, synthetic intelligence, and sport growth.

Robotic arms are highly regarded resulting from their pace, precision, and skill to work in hazardous environments. They’re used for duties corresponding to welding, meeting, and materials dealing with, particularly in industrial settings (like manufacturing).

There are two methods for a robotic to carry out a job:

Handbook Management – requires a human to explicitly program each motion. Higher suited to mounted duties, however struggles with uncertainty and requires tedious parameter tuning for each new state of affairs.

Synthetic Intelligence – permits a robotic to study one of the best actions by way of trial and error to realize a objective. So, it may well adapt to altering environments by studying from rewards and penalties with out a predefined plan (Reinforcement Learning).

On this article, I’m going to indicate methods to **construct a 3D setting with PyBullet for manually controlling a robotic arm**. I’ll current some helpful Python code that may be simply utilized in different comparable instances (simply copy, paste, run) and stroll by way of each line of code with feedback so to replicate this instance.

Setup

PyBullet can simply be put in with one of many following instructions (if pip fails, conda ought to undoubtedly work):

pip set up pybullet

conda set up -c conda-forge pybullet

PyBullet comes with a group of preset URDF information (Unified Robotic Description Format), that are XML schemas describing the bodily construction of objects within the 3D setting (i.e. dice, sphere, robotic).

import pybullet as p
import pybullet_data
import time

## setup
p.join(p.GUI)
p.resetSimulation()
p.setGravity(gravX=0, gravY=0, gravZ=-9.8)
p.setAdditionalSearchPath(path=pybullet_data.getDataPath())

## load URDF
aircraft = p.loadURDF("aircraft.urdf")

dice = p.loadURDF("dice.urdf", basePosition=[0,0,0.1], globalScaling=0.5, useFixedBase=True)

obj = p.loadURDF("cube_small.urdf", basePosition=[1,1,0.1], globalScaling=1.5)
p.changeVisualShape(objectUniqueId=obj, linkIndex=-1, rgbaColor=[1,0,0,1]) #purple

whereas p.isConnected():
    p.setRealTimeSimulation(True)

Let’s undergo the code above:

when you possibly can hook up with the physics engine, it’s essential to specify if you wish to open the graphic interface (p.GUI) or not (p.DIRECT).
The Cartesian house has 3 dimensions: x-axis (ahead/backward), y-axis (left/proper), z-axis (up/down).
It’s widespread apply to set the gravity to (x=0, y=0, z=-9.8) simulating Earth’s gravity, however one can change it primarily based on the target of the simulation.
Often, it’s essential to import a aircraft to create a floor, in any other case objects would simply fall indefinitely. If you would like an object to be mounted to the ground, then set useFixedBase=True (it’s False by default). I imported the objects with basePosition=[0,0,0.1] that means that they’re 10cm above the bottom.
The simulation may be rendered in real-time with p.setRealTimeSimulation(True) or quicker (CPU-time) with p.stepSimulation(). One other technique to set real-time is:

import time

for _ in vary(240):   #240 timestep generally utilized in videogame growth
    p.stepSimulation() #add a physics step (CPU pace = 0.1 second)
    time.sleep(1/240)  #decelerate to real-time (240 steps × 1/240 second sleep = 1 second)

Robotic

At the moment, our 3D setting is manufactured from a little bit object (tiny purple dice), and a desk (huge dice) mounted to the bottom (aircraft). I shall add a robotic arm with the duty of choosing up the smaller object and placing it on the desk.

PyBullet has a default robotic arm modeled after the Franka Panda Robot.

robo = p.loadURDF("franka_panda/panda.urdf", 
                   basePosition=[1,0,0.1], useFixedBase=True)

The very first thing to do is to research the hyperlinks (the strong elements) and joints (connections between two inflexible elements) of the robotic. For this objective, you possibly can simply use p.DIRECT as there is no such thing as a want for the graphic interface.

import pybullet as p
import pybullet_data

## setup
p.join(p.DIRECT)
p.resetSimulation()
p.setGravity(gravX=0, gravY=0, gravZ=-9.8)
p.setAdditionalSearchPath(path=pybullet_data.getDataPath())

## load URDF
robo = p.loadURDF("franka_panda/panda.urdf", 
                  basePosition=[1,0,0.1], useFixedBase=True)

## joints
dic_info = {
    0:"joint Index",  #begins at 0
    1:"joint Identify",
    2:"joint Kind",  #0=revolute (rotational), 1=prismatic (sliding), 4=mounted
    3:"state vectorIndex",
    4:"velocity vectorIndex",
    5:"flags",  #nvm all the time 0
    6:"joint Damping",  
    7:"joint Friction",  #coefficient
    8:"joint lowerLimit",  #min angle
    9:"joint upperLimit",  #max angle
    10:"joint maxForce",  #max power allowed
    11:"joint maxVelocity",  #max pace
    12:"hyperlink Identify",  #baby hyperlink linked to this joint
    13:"joint Axis",
    14:"mum or dad FramePos",  #place
    15:"mum or dad FrameOrn",  #orientation
    16:"mum or dad Index"  #−1 = base
}
for i in vary(p.getNumJoints(bodyUniqueId=robo)):
    joint_info = p.getJointInfo(bodyUniqueId=robo, jointIndex=i)
    print(f"--- JOINT {i} ---")
    print({dic_info[k]:joint_info[k] for ok in dic_info.keys()})

## hyperlinks
for i in vary(p.getNumJoints(robo)):
    link_name = p.getJointInfo(robo, i)[12].decode('utf-8')  #subject 12="hyperlink Identify"
    dyn = p.getDynamicsInfo(robo, i)
    pos, orn, *_ = p.getLinkState(robo, i)
    dic_info = {"Mass":dyn[0], "Friction":dyn[1], "Place":pos, "Orientation":orn}
    print(f"--- LINK {i}: {link_name} ---")
    print(dic_info)

Each robotic has a “base“, the foundation a part of its physique that connects the whole lot (like our skeleton backbone). Trying on the output of the code above, we all know that the robotic arm has 12 joints and 12 hyperlinks. They’re linked like this:

Motion Management

To be able to make a robotic do one thing, you must transfer its joints. There are 3 predominant sorts of management, that are all purposes of Newton’s laws of motion:

Place management: mainly, you inform the robotic “go right here”. Technically, you set a goal place, after which apply power to regularly transfer the joint from its present place towards the goal. Because it will get nearer, the utilized power decreases to keep away from overshooting and ultimately balances completely in opposition to resistive forces (i.e. friction, gravity) to carry the joint regular in place.
Velocity management: mainly, you inform the robotic “transfer at this pace”. Technically, you set a goal velocity, and apply power to regularly deliver the rate from zero to the goal, then keep it by balancing utilized power and resistive forces (i.e. friction, gravity).
Drive/Torque management: mainly, you “push the robotic and see what occurs”. Technically, you straight set the power utilized on the joint, and the ensuing movement relies upon purely on the thing’s mass, inertia, and the setting. As a aspect word, in physics, the phrase “power” is used for linear movement (push/pull), whereas “torque” signifies rotational movement (twist/flip).

We are able to use p.setJointMotorControl2() to maneuver a single joint, and p.setJointMotorControlArray() to use power to a number of joints on the identical time. As an example, I shall carry out place management by giving a random goal for every arm joint.

## setup
p.join(p.GUI)
p.resetSimulation()
p.setGravity(gravX=0, gravY=0, gravZ=-9.8)
p.setAdditionalSearchPath(path=pybullet_data.getDataPath())

## load URDF
aircraft = p.loadURDF("aircraft.urdf")
dice = p.loadURDF("dice.urdf", basePosition=[0,0,0.1], globalScaling=0.5, useFixedBase=True)
robo = p.loadURDF("franka_panda/panda.urdf", basePosition=[1,0,0.1], useFixedBase=True)
obj = p.loadURDF("cube_small.urdf", basePosition=[1,1,0.1], globalScaling=1.5)
p.changeVisualShape(objectUniqueId=obj, linkIndex=-1, rgbaColor=[1,0,0,1]) #purple

## transfer arm
joints = [0,1,2,3,4,5,6]
target_positions = [1,1,1,1,1,1,1] #<--random numbers
p.setJointMotorControlArray(bodyUniqueId=robo, jointIndices=joints,
                            controlMode=p.POSITION_CONTROL,
                            targetPositions=target_positions,
                            forces=[50]*len(joints))
for _ in vary(240*3):
    p.stepSimulation()
    time.sleep(1/240)

The true query is, “what’s the best goal place for every joint?” The reply is Inverse Kinematics, the mathematical means of calculating the parameters wanted to put a robotic in a given place and orientation relative to a place to begin. Every joint defines an angle, and also you don’t wish to guess a number of joint angles by hand. So, we’ll ask PyBullet to determine the goal positions within the Cartesian house with p.calculateInverseKinematics().

obj_position, _ = p.getBasePositionAndOrientation(obj)
obj_position = record(obj_position)

target_positions = p.calculateInverseKinematics(
    bodyUniqueId=robo,
    endEffectorLinkIndex=11, #grasp-target hyperlink
    targetPosition=[obj_position[0], obj_position[1], obj_position[2]+0.25], #25cm above object
    targetOrientation=p.getQuaternionFromEuler([0,-3.14,0]) #[roll,pitch,yaw]=[0,-π,0] -> hand pointing down
)

arm_joints = [0,1,2,3,4,5,6]
p.setJointMotorControlArray(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                            jointIndices=arm_joints,
                            targetPositions=target_positions[:len(arm_joints)],
                            forces=[50]*len(arm_joints))

Please word that I used p.getQuaternionFromEuler() to convert the 3D angles (easy for humans to understand) into 4D, as “quaternions” are simpler for physics engines to compute. If you wish to get technical, in a quaternion (x, y, z, w), the primary three parts describe the axis of rotation, whereas the fourth dimension encodes the quantity of rotation (cos/sin).

The code above instructions the robotic to maneuver its hand to a particular place above an object utilizing Inverse Kinematics. We seize the place the little purple dice is sitting on this planet with p.getBasePositionAndOrientation(), and we use the knowledge to calculate the goal place for the joints.

Work together with Objects

The robotic arm has a hand (“gripper”), so it may be opened and closed to seize objects. From the earlier evaluation, we all know that the “fingers” can transfer inside (0-0.04). So, you possibly can set the goal place because the decrease restrict (hand closed) or the higher restrict (hand open).

Furthermore, I wish to be sure that the arm holds the little purple dice whereas transferring round. You should use p.createConstraint() to make a momentary physics bond between the robotic’s gripper and the thing, so that it’s going to transfer along with the robotic hand. In the true world, the gripper would apply power by way of friction and speak to to squeeze the thing so it doesn’t fall. However, since PyBullet is a quite simple simulator, I’ll simply take this shortcut.

## shut hand
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=9, #finger_joint1
                        targetPosition=0, #decrease restrict for finger_joint1
                        power=power)
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=10, #finger_joint2
                        targetPosition=0, #decrease restrict for finger_joint2
                        power=power)

## maintain the thing
constraint = p.createConstraint(
    parentBodyUniqueId=robo,
    parentLinkIndex=11,
    childBodyUniqueId=obj,
    childLinkIndex=-1,
    jointType=p.JOINT_FIXED,
    jointAxis=[0,0,0],
    parentFramePosition=[0,0,0],
    childFramePosition=[0,0,0,1]
)

After that, we will transfer the arm towards the desk, utilizing the identical technique as earlier than: Inverse Kinematics -> goal place -> place management.

Lastly, when the robotic reaches the goal place within the Cartesian house, we will open the hand and launch the constraint between the thing and the arm.

## shut hand
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=9, #finger_joint1
                        targetPosition=0.04, #higher restrict for finger_joint1
                        power=power)
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=10, #finger_joint2
                        targetPosition=0.04, #higher restrict for finger_joint2
                        power=power)

## drop the obj
p.removeConstraint(constraint)

Full Handbook Management

In PyBullet, it’s essential to render the simulation for each motion the robotic takes. Subsequently, it’s handy to have a utils perform that may pace up (i.e. sec=0.1) or decelerate (i.e. sec=2) the real-time (sec=1).

import pybullet as p
import time

def render(sec=1):
    for _ in vary(int(240*sec)):
        p.stepSimulation()
        time.sleep(1/240)

Right here’s the total code for the simulation now we have completed on this article.

import pybullet_data

## setup
p.join(p.GUI)
p.resetSimulation()
p.setGravity(gravX=0, gravY=0, gravZ=-9.8)
p.setAdditionalSearchPath(path=pybullet_data.getDataPath())

aircraft = p.loadURDF("aircraft.urdf")
dice = p.loadURDF("dice.urdf", basePosition=[0,0,0.1], globalScaling=0.5, useFixedBase=True)
robo = p.loadURDF("franka_panda/panda.urdf", basePosition=[1,0,0.1], globalScaling=1.3, useFixedBase=True)
obj = p.loadURDF("cube_small.urdf", basePosition=[1,1,0.1], globalScaling=1.5)
p.changeVisualShape(objectUniqueId=obj, linkIndex=-1, rgbaColor=[1,0,0,1])

render(0.1)
power = 100


## open hand
print("### open hand ###")
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=9, #finger_joint1
                        targetPosition=0.04, #higher restrict for finger_joint1
                        power=power)
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=10, #finger_joint2
                        targetPosition=0.04, #higher restrict for finger_joint2
                        power=power)
render(0.1)
print(" ")


## transfer arm
print("### transfer arm ### ")
obj_position, _ = p.getBasePositionAndOrientation(obj)
obj_position = record(obj_position)

target_positions = p.calculateInverseKinematics(
    bodyUniqueId=robo,
    endEffectorLinkIndex=11, #grasp-target hyperlink
    targetPosition=[obj_position[0], obj_position[1], obj_position[2]+0.25], #25cm above object
    targetOrientation=p.getQuaternionFromEuler([0,-3.14,0]) #[roll,pitch,yaw]=[0,-π,0] -> hand pointing down
)
print("goal place:", target_positions)

arm_joints = [0,1,2,3,4,5,6]
p.setJointMotorControlArray(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                            jointIndices=arm_joints,
                            targetPositions=target_positions[:len(arm_joints)],
                            forces=[force/3]*len(arm_joints))

render(0.5)
print(" ")


## shut hand
print("### shut hand ###")
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=9, #finger_joint1
                        targetPosition=0, #decrease restrict for finger_joint1
                        power=power)
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=10, #finger_joint2
                        targetPosition=0, #decrease restrict for finger_joint2
                        power=power)
render(0.1)
print(" ")


## maintain the thing
print("### maintain the thing ###")
constraint = p.createConstraint(
    parentBodyUniqueId=robo,
    parentLinkIndex=11,
    childBodyUniqueId=obj,
    childLinkIndex=-1,
    jointType=p.JOINT_FIXED,
    jointAxis=[0,0,0],
    parentFramePosition=[0,0,0],
    childFramePosition=[0,0,0,1]
)
render(0.1)
print(" ")


## transfer in the direction of the desk
print("### transfer in the direction of the desk ###")
cube_position, _ = p.getBasePositionAndOrientation(dice)
cube_position = record(cube_position)

target_positions = p.calculateInverseKinematics(
    bodyUniqueId=robo,
    endEffectorLinkIndex=11, #grasp-target hyperlink
    targetPosition=[cube_position[0], cube_position[1], cube_position[2]+0.80], #80cm above the desk
    targetOrientation=p.getQuaternionFromEuler([0,-3.14,0]) #[roll,pitch,yaw]=[0,-π,0] -> hand pointing down
)
print("goal place:", target_positions)

arm_joints = [0,1,2,3,4,5,6]
p.setJointMotorControlArray(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                            jointIndices=arm_joints,
                            targetPositions=target_positions[:len(arm_joints)],
                            forces=[force*3]*len(arm_joints))
render()
print(" ")


## open hand and drop the obj
print("### open hand and drop the obj ###")
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=9, #finger_joint1
                        targetPosition=0.04, #higher restrict for finger_joint1
                        power=power)
p.setJointMotorControl2(bodyUniqueId=robo, controlMode=p.POSITION_CONTROL,
                        jointIndex=10, #finger_joint2
                        targetPosition=0.04, #higher restrict for finger_joint2
                        power=power)
p.removeConstraint(constraint)
render()

Conclusion

This text has been a tutorial on methods to manually management a robotic arm. We discovered about motion management, Inverse Kinematics, grabbing and transferring objects. New tutorials with extra superior robots will come.

Full code for this text: GitHub

I hope you loved it! Be at liberty to contact me for questions and suggestions, or simply to share your fascinating initiatives.

👉 Let’s Connect 👈

_{^{(All photographs are by the writer until in any other case famous)}}

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Setup

Robotic

Motion Management

Work together with Objects

Full Handbook Management

Conclusion

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