Category Archives: vrep

ABR Control repo public release!

https://github.com/abr/abr_control

Last August I started working on a new version of my old control repo with a resolve to make something less hacky, as part of the work for Applied Brain Research, Inc, a startup that I co-founded with others from my lab after most of my cohort graduated. Together with Pawel Jaworski, who comprises other half of ABR’s motor team, we’ve been building up a library of useful functions for modeling, simulating, interfacing, and controlling robot arms.

Today we’re making the repository public, under the same free for non-commercial use that we’ve released our Nengo neural modelling software on. You can find it here: ABR_Control

It’s all Python 3, and here’s an overview of some of the features:

  • Automates generation of functions for computing the Jacobians, joint space and task space inertia matrices, centrifugal and Coriolis effects, and Jacobian derivative, provided each link’s inertia matrix and the transformation matrices
  • Option to compile these functions to speedy Cython implementations
  • Operational space, joint space, floating, and sliding controllers provided with PyGame and VREP example scripts
  • Obstacle avoidance and dynamics adaptation add-ons with PyGame example scripts
  • Interfaces with VREP
  • Configuration files for one, two, and three link arms, as well as the UR5 and Jaco2 arms in VREP
  • Provides Python simulations of two and three link arms, with PyGame visualization
  • Path planning using first and second order filtering of the target and example scripts.

Structure

The ABR Control library is divided into three sections:

  1. Arm descriptions (and simulations)
  2. Robotic system interfaces
  3. Controllers

The big goal was to make all of these interchangeable, so that to run any permutation of them you just have to change which arm / interface / controller you’re importing.

To support a new arm, the user only needs to create a configuration file specifying the transforms and inertia matrices. Code for calculating the necessary functions of the arm will be symbolically derived using SymPy, and compiled to C using Cython for efficient run-time execution.

Interfaces provide send_forces and send_target_angles functions, to apply torques and put the arm in a target state, as well as a get_feedback function, which returns a dictionary of information about the current state of the arm (joint angles and velocities at a minimum).

Controllers provide a generate function, which take in current system state information and a target, and return a set of joint torques to apply to the robot arm.

VREP example

The easiest way to show it is with some code examples. So, once you’ve cloned and installed the repo, you can open up VREP and the jaco2.ttt model in the abr_control/arms/jaco2 folder, and to control it using an operational space controller you would run the following:

import numpy as np
from abr_control.arms import jaco2 as arm
from abr_control.controllers import OSC
from abr_control.interfaces import VREP

# initialize our robot config for the ur5
robot_config = arm.Config(use_cython=True, hand_attached=True)

# instantiate controller
ctrlr = OSC(robot_config, kp=200, vmax=0.5)

# create our VREP interface
interface = VREP(robot_config, dt=.001)
interface.connect()

target_xyz = np.array([0.2, 0.2, 0.2])
# set the target object's position in VREP
interface.set_xyz(name='target', xyz=target_xyz)

count = 0.0
while count < 1500:  # run for 1.5 simulated seconds
    # get joint angle and velocity feedback
    feedback = interface.get_feedback()
    # calculate the control signal
    u = ctrlr.generate(
        q=feedback['q'],
        dq=feedback['dq'],
        target_pos=target_xyz)
    # send forces into VREP, step the sim forward
    interface.send_forces(u)

    count += 1
interface.disconnect()

This is a minimal example of the examples/VREP/reaching.py code. To run it with a different arm, you can just change the from abr_control.arms import as line. The repo comes with the configuration files for the UR5 and a onelink VREP arm model as well.

PyGame example

I’ve also found the PyGame simulations of the 2 and 3 link arms very helpful for quickly testing new controllers and code, as an easy low overhead proof of concept sandbox. To run the threelink arm (which runs in Linux and Windows fine but I’ve heard has issues in Mac OS), with the operational space controller, you can run this script:

import numpy as np
from abr_control.arms import threelink as arm
from abr_control.interfaces import PyGame
from abr_control.controllers import OSC

# initialize our robot config
robot_config = arm.Config(use_cython=True)
# create our arm simulation
arm_sim = arm.ArmSim(robot_config)

# create an operational space controller
ctrlr = OSC(robot_config, kp=300, vmax=100,
            use_dJ=False, use_C=True)

def on_click(self, mouse_x, mouse_y):
    self.target[0] = self.mouse_x
    self.target[1] = self.mouse_y

# create our interface
interface = PyGame(robot_config, arm_sim, dt=.001, 
                   on_click=on_click)
interface.connect()

# create a target
feedback = interface.get_feedback()
target_xyz = robot_config.Tx('EE', feedback['q'])
interface.set_target(target_xyz)

try:
    while 1:
        # get arm feedback
        feedback = interface.get_feedback()
        hand_xyz = robot_config.Tx('EE', feedback['q'])

        # generate an operational space control signal
        u = ctrlr.generate(
            q=feedback['q'],
            dq=feedback['dq'],
            target_pos=target_xyz)

        new_target = interface.get_mousexy()
        if new_target is not None:
            target_xyz[0:2] = new_target
        interface.set_target(target_xyz)

        # apply the control signal, step the sim forward
        interface.send_forces(u)

finally:
    # stop and reset the simulation
    interface.disconnect()

The extra bits of code just set up a hook so that when you click on the PyGame display somewhere the target moves to that point.

So! Hopefully some people find this useful for their research! It should be as easy to set up as cloning the repo, installing the requirements and running the setup file, and then trying out the examples.

If you find a bug please file an issue! If you find a way to improve it please do so and make a PR! And if you’d like to use anything in the repo commercially, please contact me.

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Full body obstacle collision avoidance

Previously I’ve discussed how to avoid obstacles using DMPs in the end-effector trajectory. This is good when you’re controlling a single disconnected point-mass, like a mobile robot navigating around an environment. But if you want to use this system to control a robotic manipulator, then pretty quickly you run into the problem that your end-effector is not a disconnected point-mass moving around the environment. It’s attached to the rest of the arm, and moving such that the arm segments and joints also avoid the obstacle is a whole other deal.

I was doing a quick lit scan on methods for control methods for avoiding joint collision with obstacles, and was kind of surprised that there wasn’t really much in the realm of recent discussions about it. There is, however, a 1986 paper from Dr. Oussama Khatib titled Real-time obstacle avoidance for manipulators and mobile robots that pretty much solves this problem short of getting into explicit path planning methods. Which could be why there aren’t papers since then about it. All the same, I couldn’t find any implementations of it online, and there are a few tricky parts, so in this post I’m going to work through the implementation.

Note: The implementation that I’ve worked out here uses spheres to represent the obstacles. This works pretty well in general by just making the sphere large enough to cover whatever obstacle you’re avoiding. But if you want a more precise control around other shapes, you can check out the appendix of Dr. Khatib’s paper, where he works provides the math for cubes and cones as well.

Note: You can find all of the code I use here and everything you need for the VREP implementation up on my GitHub. I’ve precalculated the functions that are required, because generating them is quite computationally intensive. Hopefully the file saving doesn’t get weird between different operating systems, but if it does, try deleting all of the files in the ur5_config folder and running the code again. This will regenerate those files (on my laptop this takes ~4 hours, though, so beware).

The general idea

Since it’s from Dr. Khatib, you might expect that this approach involves task space. And indeed, your possible suspicions are correct! The algorithm is going to work by identifying forces in Cartesian coordinates that will move any point of the arm that’s too close to an obstacle away from it. The algorithm follows the form:

Setup

  • Specify obstacle location and size
  • Specify a threshold distance to the obstacle

While running

  • Find the closest point of each arm segment to obstacles
  • If within threshold of obstacle, generate force to apply to that point
  • Transform this force into joint torques
  • Add directly to the outgoing control signal

Something that you might notice about this is that it’s similar to the addition of the null space controller that we’ve seen before in operational space control. There’s a distinct difference here though, in that the control signal for avoiding obstacles is added directly to the outgoing control signal, and that it’s not filtered (like the null space control signal) such that there’s no guarantee that it won’t affect the movement of the end-effector. In fact, it’s very likely to affect the movement of the end-effector, but that’s also desirable, as not ramming the arm through an obstacle is as important as getting to the target.

OK, let’s walk through these steps one at a time.

Setup

I mentioned that we’re going to treat all of our obstacles as spheres. It’s actually not much harder to do these calculations for cubes too, but this is already going to be long enough so I’m only addressing sphere’s here. This algorithm assumes we have a list of every obstacle’s centre-point and radius.

We want the avoidance response of the system to be a function of the distance to the obstacle, such that the closer the arm is to the obstacle the stronger the response. The function that Dr. Khatib provides is of the following form:

\textbf{F}_{psp} = \left\{ \begin{array}{cc}\eta (\frac{1.0}{\rho} - \frac{1}{\rho_0}) \frac{1}{\rho^2} \frac{\partial \rho}{\partial \textbf{x}} & \rho \leq \rho_0 \\ \textbf{0} & \rho > \rho_0 \end{array} \right. ,

where \rho is the distance to the target, \rho_0 is the threshold distance to the target at which point the avoidance function activates, \frac{\partial \rho}{\partial \textbf{x}} is the partial derivative of the distance to the target with respect to a given point on the arm, and \eta is a gain term.

This function looks complicated, but it’s actually pretty intuitive. The partial derivative term in the function works simply to point in the opposite direction of the obstacle, in Cartesian coordinates, i.e. tells the system how to get away from the obstacle. The rest of the term is just a gain that starts out at zero when \rho = \rho_0, and gets huge as the obstacle nears the object (as \rho \to 0 \Rightarrow \frac{1}{\rho} \to \infty). Using \eta = .2 and \rho_0 = .2 gives us a function that looks like

gain

So you can see that very quickly a very, very strong push away from this obstacle is going to be generated once we enter the threshold distance. But how do we know exactly when we’ve entered the threshold distance?

Find the closest point

We want to avoid the obstacle with our whole body, but it turns out we can reduce the problem to only worrying about the closest point of each arm segment to the obstacle, and move that one point away from the obstacle if threshold distance is hit.

To find the closest point on a given arm segment to the obstacle we’ll do some pretty neat trig. I’ll post the code for it and then discuss it below. In this snippet, p1 and p2 are the beginning and ending (x,y,z) locations of arm segment (which we are assuming is a straight line), and v is the center of the obstacle.

# the vector of our line
vec_line = p2 - p1
# the vector from the obstacle to the first line point
vec_ob_line = v - p1
# calculate the projection normalized by length of arm segment
projection = (np.dot(vec_ob_line, vec_line) /
              np.dot(vec_line, vec_line))
if projection < 0:               
    # then closest point is the start of the segment
    closest = p1  
elif projection > 1:
    # then closest point is the end of the segment
    closest = p2
else:
    closest = p1 + projection * vec_line

The first thing we do is find the arm segment line, and then line from the obstacle center to the start point of the arm segment. Once we have these, we do:

\frac{\textbf{v}_\textrm{ob\_line} \; \cdot \; \textbf{v}_\textrm{line}}{\textbf{v}_\textrm{line} \; \cdot \; \textbf{v}_\textrm{line}},

using the geometric definition of the dot product two vectors, we can rewrite the above as

\frac{||\textbf{v}_\textrm{ob\_line}|| \; || \textbf{v}_\textrm{line} || \; \textrm{cos}(\theta)}{||\textbf{v}_\textrm{line}||^2} = \frac{||\textbf{v}_\textrm{ob\_line}||} {||\textbf{v}_\textrm{line}||} \textrm{cos}(\theta)

which reads as the magnitude of vec_ob_line divided by the magnitude of vec_line (I know, these are terrible names, sorry) multiplied by the angle between the two vectors. If the angle between the vectors is < 0 (projection will also be < 0), then right off the bat we know that the start of the arm segment, p1, is the closest point. If the projection value is > 1, then we know that 1) the length from the start of the arm segment to the obstacle is longer than the length of the arm, and 2) the angle is such that the end of the arm segment, p2, is the closest point to the obstacle.

Finally, in the last case we know that the closest point is somewhere along the arm segment. To find where exactly, we do the following

\textbf{p}_1 + \textrm{projection} \; \textbf{v}_\textrm{line},

which can be rewritten

\textbf{p}_1 + \frac{||\textbf{v}_\textrm{ob\_line}||} {||\textbf{v}_\textrm{line}||} \textrm{cos}(\theta) \; \textbf{v}_\textrm{line},

I find it more intuitive if we rearrange the second term to be

\textbf{p}_1 + \frac{\textbf{v}_\textrm{line}} {||\textbf{v}_\textrm{line}||} \; ||\textbf{v}_\textrm{ob\_line} || \; \textrm{cos}(\theta).

So then what this is all doing is starting us off at the beginning of the arm segment, p1, and to that we add this other fun term. The first part of this fun term provides direction normalized to magnitude 1. The second part of this term provides magnitude, specifically the exact distance along vec_line we need to traverse to form reach a right angle (giving us the shortest distance) pointing at the obstacle. This handy figure from the Wikipedia page helps illustrate exactly what’s going on with the second part, where B is be vec_line and A is vec_ob_line:

dot_product
Armed with this information, we understand how to find the closest point of each arm segment to the obstacle, and we are now ready to generate a force to move the arm in the opposite direction!

Check distance, generate force

To calculate the distance, all we have to do is calculate the Euclidean distance from the closest point of the arm segment to the center of the sphere, and then subtract out the radius of the sphere:

# calculate distance from obstacle vertex to the closest point
dist = np.sqrt(np.sum((v - closest)**2))
# account for size of obstacle
rho = dist - obstacle[3]

Once we have this, we can check it and generate F_{psp} using the equation we defined above. The one part of that equation that wasn’t specified was exactly what \frac{\partial \rho}{\partial \textbf{x}} was. Since it’s just the partial derivative of the distance to the target with respect to the closest point, we can calculate it as the normalized difference between the two points:

drhodx = (v - closest) / rho

Alright! Now we’ve found the closest point, and know the force we want to apply, from here it’s standard operational space procedure.

Transform the force into torques

As we all remember, the equation for transforming a control signal from operational space to involves two terms aside from the desired force. Namely, the Jacobian and the operational space inertia matrix:

\textbf{u}_\textrm{psp} = \textbf{J}^T_{psp} \textbf{M}_{psp} \textbf{F}_{psp},

where \textbf{J}_{psp} is the Jacobian for the point of interest, \textbf{M}_{psp} is the operational space inertia matrix for the point of interest, and \textbf{F}_{psp} is the force we defined above.

Calculating the Jacobian for an unspecified point

So the first thing we need to calculate is the Jacobian for this point on the arm. There are a bunch of ways you could go about this, but the way I’m going to do it here is by building on the post where I used SymPy to automate the Jacobian derivation. The way we did that was by defining the transforms from the origin reference frame to the first link, from the first link to the second, etc, until we reached the end-effector. Then, whenever we needed a Jacobian we could string together the transforms to get the transform from the origin to that point on the arm, and take the partial derivative with respect to the joints (using SymPy’s derivative method).

As an example, say we wanted to calculate the Jacobian for the third joint, we would first calculate:

^3_{\textrm{org}}\textbf{T} = ^0_{\textrm{org}}\textbf{T} \; ^1_0\textbf{T} \; ^2_1\textbf{T} \; ^3_2\textbf{T},

where ^m_n\textbf{T} reads the transform from reference frame m to reference frame n.

Once we have this transformation matrix, ^3_\textrm{org}\textbf{T}, we multiply it by the point of interest in reference frame 3, which, previously, has usually been \textbf{x} = [0, 0, 0]. In other words, usually we’re just interested in the origin of reference frame 3. So the Jacobian is just

\frac{\partial \; ^3_\textrm{org}\textbf{T} \textbf{x}}{\partial \textbf{q}}.

what if we’re interested in some non-specified point along link 3, though? Well, using SymPy we set make \textbf{x} = [x_0, x_1, x_2, 1] instead of \textbf{x} = [0, 0, 0, 1] (recall the 1 at the end in these vectors is just to make the math work), and make the Jacobian function SymPy generates for us dependent on both \textbf{q} and \textbf{x}, rather than just \textbf{q}. In code this looks like:

Torg3 = self._calc_T(name="3")
# transform x into world coordinates
Torg3x = sp.simplify(Torg3 * sp.Matrix(x))
J3_func = sp.lambdify(q + x, Torg3)

Now it’s possible to calculate the Jacobian for any point along link 3 just by changing the parameters that we pass into J3_func! Most excellent.

We are getting closer.

NOTE: This parameterization can significantly increase the build time of the function, it took my laptop about 4 hours. To decrease build time you can try commenting out the simplify calls from the code, which might slow down run-time a bit but significantly drops the generation time.

Where is the closest point in that link’s reference frame?

A sneaky problem comes up when calculating the closest point of each arm segment to the object: We’ve calculated the closest point of each arm segment in the origin’s frame of reference, and we need thew relative to each link’s own frame of reference. Fortunately, all we have to do is calculate the inverse transform for the link of interest. For example, the inverse transform of ^3_\textrm{org}\textbf{T} transforms a point from the origin’s frame of reference to the reference frame of the 3rd joint.

I go over how to calculate the inverse transform at the end of my post on forward transformation matrices, but to save you from having to go back and look through that, here’s the code to do it:

Torg3 = self._calc_T(name="3")
rotation_inv = Torg3[:3, :3].T
translation_inv = -rotation_inv * Torg3[:3, 3]
Torg3_inv = rotation_inv.row_join(translation_inv).col_join(
    sp.Matrix([[0, 0, 0, 1]]))

And now to find the closest point in the coordinates of reference frame 3 we simply

x = np.dot(Torg3_inv, closest)

This x value is what we’re going to plug in as parameters to our J3_func above to find the Jacobian for the closest point on link 3.

Calculate the operational space inertia matrix for the closest point

OK. With the Jacobian for the point of interest we are now able to calculate the operational space inertia matrix. This code I’ve explicitly worked through before, and I’ll show it in the full code below, so I won’t go over it again here.

The whole implementation

You can run an example of all this code controlling the UR5 arm to avoid obstacles in VREP using this code up on my GitHub. The specific code added to implement obstacle avoidance looks like this:

# find the closest point of each link to the obstacle
for ii in range(robot_config.num_joints):
    # get the start and end-points of the arm segment
    p1 = robot_config.Tx('joint%i' % ii, q=q)
    if ii == robot_config.num_joints - 1:
        p2 = robot_config.Tx('EE', q=q)
    else:
        p2 = robot_config.Tx('joint%i' % (ii + 1), q=q)

    # calculate minimum distance from arm segment to obstacle
    # the vector of our line
    vec_line = p2 - p1
    # the vector from the obstacle to the first line point
    vec_ob_line = v - p1
    # calculate the projection normalized by length of arm segment
    projection = (np.dot(vec_ob_line, vec_line) /
                  np.sum((vec_line)**2))
    if projection < 0:         
        # then closest point is the start of the segment
        closest = p1
    elif projection > 1:
        # then closest point is the end of the segment
        closest = p2
    else:
        closest = p1 + projection * vec_line
    # calculate distance from obstacle vertex to the closest point
    dist = np.sqrt(np.sum((v - closest)**2))
    # account for size of obstacle
    rho = dist - obstacle_radius

    if rho < threshold:
        eta = .02
        drhodx = (v - closest) / rho
        Fpsp = (eta * (1.0/rho - 1.0/threshold) *
                1.0/rho**2 * drhodx)

        # get offset of closest point from link's reference frame
        T_inv = robot_config.T_inv('link%i' % ii, q=q)
        m = np.dot(T_inv, np.hstack([closest, [1]]))[:-1]
        # calculate the Jacobian for this point
        Jpsp = robot_config.J('link%i' % ii, x=m, q=q)[:3]

        # calculate the inertia matrix for the
        # point subjected to the potential space
        Mxpsp_inv = np.dot(Jpsp,
                        np.dot(np.linalg.pinv(Mq), Jpsp.T))
        svd_u, svd_s, svd_v = np.linalg.svd(Mxpsp_inv)
        # cut off singular values that could cause problems
        singularity_thresh = .00025
        for ii in range(len(svd_s)):
            svd_s[ii] = 0 if svd_s[ii] < singularity_thresh else \
                1./float(svd_s[ii])
        # numpy returns U,S,V.T, so have to transpose both here
        Mxpsp = np.dot(svd_v.T, np.dot(np.diag(svd_s), svd_u.T))

        u_psp = -np.dot(Jpsp.T, np.dot(Mxpsp, Fpsp))
        if rho < .01:
            u = u_psp
        else:
            u += u_psp

The one thing in this code I didn’t talk about is that you can see that if rho < .01 then I set u = u_psp instead of just adding u_psp to u. What this does is basically add in a fail safe take over of the robotic control saying that “if we are about to hit the obstacle forget about everything else and get out of the way!”.

Results

And that’s it! I really enjoy how this looks when it’s running, it’s a really effective algorithm. Let’s look at some samples of it in action.

First, in a 2D environment, where it’s real easy to move around the obstacle and see how it changes in response to the new obstacle position. The red circle is the target and the blue circle is the obstacle:
avoid2d

And in 3D in VREP, running the code example that I’ve put up on my GitHub implementing this. The example of it running without obstacle avoidance code is on the left, and running with obstacle avoidance is on the right. It’s kind of hard to see but on the left the robot moves through the far side of the obstacle (the gold sphere) on its way to the target (the red sphere):

And one last example, the arm dodging a moving obstacle on its way to the target.

movingavoid3d

The implementation is a ton of fun to play around with. It’s a really nifty algorithm, that works quite well, and I haven’t found many alternatives in papers that don’t go into path planning (if you know of some and can share that’d be great!). This post was a bit of a journey, but hopefully you found it useful! I continue to find it impressive how many different neat features like this can come about once you have the operational space control framework in place.

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Velocity limiting in operational space control

Recently, I was reading through an older paper on effective operational space control, talking specifically point to point control in operational space. The paper mentioned that even if you have a perfect model of the system, you’re going to run into trouble if you use just a basic PD formula to define your control signal in operational space:

u_x = k_p (\textbf{x}^* - \textbf{x}) - k_v \dot{\textbf{x}},

where \textbf{x} and \dot{\textbf{x}} are the system position and velocity in operational space, \textbf{x}^* is the target position, and k_p and k_v are gains.

If you define your operational space control signal like this, and then translate this signal into joint torques (using, for example, methods discussed in other posts), you’re going to see a very non-straight trajectory emerge in larger movements as a result of “actuator saturation, and bandwidth and velocity limitations”. In the example of a standard robot, you might run into issues with your motors not being able to actually generate the torques that have been specified, the frequency of control and feedback might not be sufficient, and you could hit hard constraints on system velocity. The solution to this problem was presented in this 1987 paper by Dr. Oussama Khatib, and is pretty slick and very useful, so I thought I’d write it up here for any other unfortunate souls wandering around in ignorance. First though, here’s what it looks like to move large point to point distances without velocity limiting:

As you can see, the system isn’t moving in a straight line, which can be very aggravating if you’ve worked and reworked out the equations and double checked all your parameters, etc etc. A few things, first, when working with simulations it’s easy to forget how ridiculously fast this actually would be in real-time. Even though it takes a minute to simulate the above movement, in real-time, is happening over the course of 200ms. Taking that into account, this is pretty good. Also, there’s an obvious solution here, slow down your movement. The source of this problem is largely that all of the motors are not able to apply the torques specified and move at the required speed. Some of the motors have a lot less mass to throw around and will be able to move at the specified torques, but not all. Hence the not straight trajectory.

You can of course drop the gains on your PD signal, but that’s not really a great methodical solution. So, what can we do?

Well, if we rearrange the PD control signal specified above into

u_x = k_v (\dot{\textbf{x}}^* - \dot{\textbf{x}}),

where \dot{\textbf{x}}^* is the desired velocity, we see that this signal can be interpreted as a pure velocity servo-control signal, with velocity gain k_v and a desired velocity

\dot{\textbf{x}}^* = \frac{k_p}{k_v}(\textbf{x}^* - \textbf{x}).

When things are in this form, it becomes a bit more clear what we have to do: limit the desired velocity to be at most some specified maximum velocity of the end-effector, V_\textrm{max}. This value should be low enough that the transformation into joint torques doesn’t result in anything larger than the actuators can generate.

Taking V_\textrm{max}, what we want is to clip the magnitude of the control signal to be V_\textrm{max} if it’s ever larger (in positive or negative directions), and to be equal to \frac{kp}{kv}(\textbf{x}^* - \textbf{x}) otherwise. The math for this works out such that we can accomplish this with a control signal of the form:

\textbf{u}_\textbf{x} = -k_v (\dot{\textbf{x}} + \textrm{sat}\left(\frac{V_\textrm{max}}{\lambda |\tilde{\textbf{x}}|} \right) \lambda \tilde{\textbf{x}}),

where \lambda = \frac{k_p}{k_v} , \tilde{\textbf{x}} = \textbf{x} - \textbf{x}^*, and \textrm{sat} is the saturation function, such that

\textrm{sat}(y) = \left\{ \begin{array}{cc} |y| \leq 1 & \Rightarrow y \\ |y| > 1 & \Rightarrow 1 \end{array} \right.

where |y| is the absolute value of y, and is applied element wise to the vector \tilde{\textbf{x}} in the control signal.

As a result of using this saturation function, the control signal behaves differently depending on whether or not \dot{\textbf{x}}^* > V_\textrm{max}:

\textbf{u}_\textbf{x} = \left\{ \begin{array}{cc} \dot{\textbf{x}}^* \geq V_\textrm{max} & \Rightarrow -k_v (\dot{\textbf{x}} + V_\textbf{max} \textrm{sgn}(\tilde{\textbf{x}})) \\ \dot{\textbf{x}}^* < V_\textrm{max} & \Rightarrow -k_v \dot{\textbf{x}} + k_p \tilde{\textbf{x}} \end{array} \right.

where \textrm{sgn}(y) is a function that returns -1 if y < 0 and 1 if y \geq 0, and is again applied element-wise to vectors. Note that the control signal in the second condition is equivalent to our original PD control signal k_p(\textbf{x}^* - \textbf{x}) - k_v \dot{\textbf{x}}. If you’re wondering about negative signs make sure you note that \tilde{\textbf{x}} = \textbf{x} - \textbf{x}^* and not \textbf{x}^* - \textbf{x}, as you might assume.

So now the control signal is behaving exactly as desired! Moves the system towards the target, but no faster than the specified maximum velocity. Now our trajectories look like this:

So those are starting to look a lot better! The first thing you’ll notice is that this is a fair bit slower of a movement. Well, actually, it’s waaaayyyy slower because the playback speed here is 4x faster than in that first animation, and this is a movement over 2s. Which has pros and cons, con: it’s slower, pro: it’s straighter, and you’re less likely to be murdered by it. When you move from simulations to real systems that latter point really moves way up the priority list.

Second thing to notice, the system seems to be minimising the error along one dimension, and then along the next, and then arrives at the target. What’s going on?  Because the error along each of the (x,y,z) dimensions isn’t the same, when speed gets clipped along one of the dimensions you’re no longer going to be moving in a straight line directly to the target. To address this, we’re going to add a scaling term whenever clipping happens, such that you reduce the speed you move along each dimension by the same ratio, so that you’re still moving in a straight line.

It’s a liiiiittle bit more complicated than that, but not much. First, we’ll calculate the values being passed in to the saturation function for each (x,y,z) dimension. We’ll then check to see if any of them are going to get clipped, and if there’s more than one that saturates we’ll find the one that is affected the most. After we’ve identified which dimension it is, we go through and calculate what the control signal would have been without velocity limiting, and what it will be now with velocity limiting. This scaling term tells us how much the control signal was reduced, and we can then use it to reduce the control signals of the other dimensions by the same amount. These other dimensions might still saturate, though, so we have to recalculate the saturation function for them once they’ve been scaled. Here’s what this all looks like in code:

# implement velocity limiting
lamb = kp / kv
x_tilde = xyz - target_xyz
sat = vmax / (lamb * np.abs(x_tilde))
scale = np.ones(3)
if np.any(sat < 1):
    index = np.argmin(sat)
    unclipped = kp * x_tilde[index]
    clipped = kv * vmax * np.sign(x_tilde[index])
    scale = np.ones(3) * clipped / unclipped
    scale[index] = 1
u_xyz = -kv * (dx + np.clip(sat / scale, 0, 1) *
               scale * lamb * x_tilde)
 

And now, finally, we start getting the trajectories that we’ve been wanting the whole time:

And finally we can rest easy, knowing that our robot is moving at a reasonable speed along a direct path to its goals. Wherever you’d like to use this neato ‘ish you should be able to just paste in the above code, define your vmax, kp, and kv values and be good to go!

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Using SymPy’s lambdify for generating transform matrices and Jacobians

I’ve been working in VREP with some of their different robot models and testing out force control, and one of the things that becomes pretty important for efficient workflow is to have a streamlined method for setting up the transform matrices and calculating the Jacobians for different robots. You do not want to be working these all out by hand and then writing them in yourself.

A solution that’s been working well for me (and is fully implemented in Python) is to use SymPy to set up the basic transform matrices, from each joint to the next, and then use it’s derivative function to calculate the Jacobian. Once this is calculated you can then use SymPy’s lambdify function to parameterize this, and off you go! In this post I’m going to work through an example for controlling VREP’s UR5 arm using force control. And if you’re just looking for code examples, you can find it all up on my GitHub.

Edit: Updated the code to be much nicer, added saving of calculated functions, and a null space controller.
Edit 2: Removed the simplify calls from the code, not worth the generation time increase (if execution is a great concern can generate the functions using cython).

Setting up the transform matrices

This is the part of this process that is unique to each arm. The goal is to set up a system so that you can specify your transforms for each joint (and to each centre-of-mass (COM) too, of course) and then be on your way. So here’s the UR5, cute thing that it is:

ur5.png

For the UR5, there are 6 joints, and we’re going to be specifying 9 transform matrices: 6 joints and the COM for three arm segments (the remaining arm segment COMs are centered at their respective joint’s reference frame). The joints are all rotary, with 0 and 4 rotating around the z-axis, and the rest all rotating around x.

So first, we’re going to create a class called robot_config. Then, to create our transform matrices in SymPy first we need to set up the variables we’re going to be using:

# set up our joint angle symbols (6th angle doesn't affect any kinematics)
self.q = [sp.Symbol('q%i'%ii) for ii in range(6)]
# segment lengths associated with each joint
self.L = np.array([0.0935, .13453, .4251, .12, .3921, .0935, .0935, .0935])

where self.q is an array storing all our joint angle symbols, and self.L is an array of all of the offsets for each joint and arm segment lengths.

Using these to create the transform matrices for the joints, we get a set up that looks like this:

# transform matrix from origin to joint 0 reference frame
self.T0org = sp.Matrix([[sp.cos(self.q[0]), -sp.sin(self.q[0]), 0, 0],
                        [sp.sin(self.q[0]), sp.cos(self.q[0]), 0, 0],
                        [0, 0, 1, self.L[0]],
                        [0, 0, 0, 1]])

# transform matrix from joint 0 to joint 1 reference frame
self.T10 = sp.Matrix([[1, 0, 0, -L[1]],
                      [0, sp.cos(-self.q[1] + sp.pi/2),
                       -sp.sin(-self.q[1] + sp.pi/2), 0],
                      [0, sp.sin(-self.q[1] + sp.pi/2),
                       sp.cos(-self.q[1] + sp.pi/2), 0],
                      [0, 0, 0, 1]])

# transform matrix from joint 1 to joint 2 reference frame
self.T21 = sp.Matrix([[1, 0, 0, 0],
                      [0, sp.cos(-self.q[2]),
                       -sp.sin(-self.q[2]), self.L[2]],
                      [0, sp.sin(-self.q[2]),
                       sp.cos(-self.q[2]), 0],
                      [0, 0, 0, 1]])

# transform matrix from joint 2 to joint 3
self.T32 = sp.Matrix([[1, 0, 0, L[3]],
                      [0, sp.cos(-self.q[3] - sp.pi/2),
                       -sp.sin(-self.q[3] - sp.pi/2), self.L[4]],
                      [0, sp.sin(-self.q[3] - sp.pi/2),
                       sp.cos(-self.q[3] - sp.pi/2), 0],
                      [0, 0, 0, 1]])

# transform matrix from joint 3 to joint 4
self.T43 = sp.Matrix([[sp.sin(-self.q[4] - sp.pi/2),
                       sp.cos(-self.q[4] - sp.pi/2), 0, -self.L[5]],
                      [sp.cos(-self.q[4] - sp.pi/2),
                       -sp.sin(-self.q[4] - sp.pi/2), 0, 0],
                      [0, 0, 1, 0],
                      [0, 0, 0, 1]])

# transform matrix from joint 4 to joint 5
self.T54 = sp.Matrix([[1, 0, 0, 0],
                      [0, sp.cos(self.q[5]), -sp.sin(self.q[5]), 0],
                      [0, sp.sin(self.q[5]), sp.cos(self.q[5]), self.L[6]],
                      [0, 0, 0, 1]])

# transform matrix from joint 5 to end-effector
self.TEE5 = sp.Matrix([[1, 0, 0, self.L[7]],
                       [0, 1, 0, 0],
                       [0, 0, 1, 0],
                       [0, 0, 0, 1]])

You can see a bunch of offsetting of the joint angles by -sp.pi/2 and this is to account for the expected 0 angle (straight along the reference frame’s x-axis) at those joints being different than the 0 angle defined in the VREP simulation (at a 90 degrees from the x-axis). You can find these by either looking at and finding the joints’ 0 position in VREP or by trial-and-error empirical analysis.

Once you have your transforms, then you have to specify how to move from the origin to each reference frame of interest (the joints and COMs). For that, I’ve just set up a simple function with a switch statement:

# point of interest in the reference frame (right at the origin)
self.x = sp.Matrix([0,0,0,1])

def _calc_T(self, name, lambdify=True):
    """ Uses Sympy to generate the transform for a joint or link

    name string: name of the joint or link, or end-effector
    lambdify boolean: if True returns a function to calculate
                      the transform. If False returns the Sympy
                      matrix
    """

    # check to see if we have our transformation saved in file
    if os.path.isfile('%s/%s.T' % (self.config_folder, name)):
        Tx = cloudpickle.load(open('%s/%s.T' % (self.config_folder, name),
                                   'rb'))
    else:
        if name == 'joint0' or name == 'link0':
            T = self.T0org
        elif name == 'joint1' or name == 'link1':
            T = self.T0org * self.T10
        elif name == 'joint2':
            T = self.T0org * self.T10 * self.T21
        elif name == 'link2':
            T = self.T0org * self.T10 * self.Tl21
        elif name == 'joint3':
            T = self.T0org * self.T10 * self.T21 * self.T32
        elif name == 'link3':
            T = self.T0org * self.T10 * self.T21 * self.Tl32
        elif name == 'joint4' or name == 'link4':
            T = self.T0org * self.T10 * self.T21 * self.T32 * self.T43
        elif name == 'joint5' or name == 'link5':
            T = self.T0org * self.T10 * self.T21 * self.T32 * self.T43 * \
                self.T54
        elif name == 'link6' or name == 'EE':
            T = self.T0org * self.T10 * self.T21 * self.T32 * self.T43 * \
                self.T54 * self.TEE5
        Tx = T * self.x  # to convert from transform matrix to (x,y,z)

        # save to file
        cloudpickle.dump(Tx, open('%s/%s.T' % (self.config_folder, name),
                                  'wb'))

    if lambdify is False:
        return Tx
    return sp.lambdify(self.q, Tx)

So the first part is pretty straight forward, create the transform matrix, and then at the end to get the (x,y,z) position we just multiply by a vector we created that represents a point at the origin of the last reference frame. Some of the transform matrices (the ones to the arm segments) I didn’t specify above just to cut down on space.

The second part is where we use this awesome lambify function, which lets us turn the matrix we’ve defined into a function, so that we can pass in joint angles and get back out the resulting (x,y,z) position. There’s also the option to get the straight up SymPy matrix return, in case you need the symbolic form (which we will!).

NOTE: You can also see that there’s a check built in to look for saved files, and to just load those saved files instead of recalculating things if they’re available. This is because calculating some of these matrices and their derivatives takes a long, long time. I used the cloudpickle module to do this because it’s able to easily handle saving a whole bunch of weird things that makes normal pickle sour.

Calculating the Jacobian

So now that we’re able to quickly generate the transform matrix for each point of interest on the UR5, we simply take the derivative of the equation for each (x,y,z) coordinate with respect to each joint angle to generate our Jacobian.

def _calc_J(self, name, lambdify=True):
    """ Uses Sympy to generate the Jacobian for a joint or link

    name string: name of the joint or link, or end-effector
    lambdify boolean: if True returns a function to calculate
                      the Jacobian. If False returns the Sympy
                      matrix
    """

    # check to see if we have our Jacobian saved in file
    if os.path.isfile('%s/%s.J' % (self.config_folder, name)):
        J = cloudpickle.load(open('%s/%s.J' %
                             (self.config_folder, name), 'rb'))
    else:
        Tx = self._calc_T(name, lambdify=False)
        J = []
        # calculate derivative of (x,y,z) wrt to each joint
        for ii in range(self.num_joints):
            J.append([])
            J[ii].append(Tx[0].diff(self.q[ii]))  # dx/dq[ii]
            J[ii].append(Tx[1].diff(self.q[ii]))  # dy/dq[ii]
            J[ii].append(Tx[2].diff(self.q[ii]))  # dz/dq[ii]

Here we retrieve the Tx vector from our _calc_T function, and then calculate the derivatives. When calculating the Jacobian for the end-effector, this is all we need! Huzzah!

But to calculate the Jacobian for transforming the inertia matrices of each arm segment into joint space we’re going to need the orientation information added to our Jacobian as well. This we know ahead of time, for each joint it’s a 3D vector with a 1 on the axis being rotated around. So we can predefine this:

# orientation part of the Jacobian (compensating for orientations)
self.J_orientation = [
    [0, 0, 1], # joint 0 rotates around z axis
    [1, 0, 0], # joint 1 rotates around x axis
    [1, 0, 0], # joint 2 rotates around x axis
    [1, 0, 0], # joint 3 rotates around x axis
    [0, 0, 1], # joint 4 rotates around z axis
    [1, 0, 0]] # joint 5 rotates around x axis

And then we just fill in the Jacobians for each reference frame with self.J_orientation up to the last joint, and fill in the rest of the Jacobian with zeros. So e.g. when we’re calculating the Jacobian for the arm segment just past the second joint we’ll use the first two rows of self.J_orientation and the rest of the rows will be 0.

So this leads us to the second half of the _calc_J function:

        end_point = name.strip('link').strip('joint')
        if end_point != 'EE':
            end_point = min(int(end_point) + 1, self.num_joints)
            # add on the orientation information up to the last joint
            for ii in range(end_point):
                J[ii] = J[ii] + self.J_orientation[ii]
            # fill in the rest of the joints orientation info with 0
            for ii in range(end_point, self.num_joints):
                J[ii] = J[ii] + [0, 0, 0]

        # save to file
        cloudpickle.dump(J, open('%s/%s.J' %
                                 (self.config_folder, name), 'wb'))

    J = sp.Matrix(J).T  # correct the orientation of J
    if lambdify is False:
        return J
    return sp.lambdify(self.q, J)

The orientation information is added in, we save the result to file, and a function that takes in the joint angles and outputs the Jacobian is created (unless lambdify == False in which case the SymPy symbolic form is returned.)

Then finally, two wrapper functions are added in to make creating / accessing these functions easier. First, define a couple of dictionaries

# create function dictionaries
self._T = {}  # for transform calculations
self._J = {}  # for Jacobian calculations

and then our wrapper functions look like this

def T(self, name, q):
    """ Calculates the transform for a joint or link
    name string: name of the joint or link, or end-effector
    q np.array: joint angles
    """
    # check for function in dictionary
    if self._T.get(name, None) is None:
        print('Generating transform function for %s'%name)
        self._T[name] = self.calc_T(name)
    return self._T[name](*q)[:-1].flatten()

def J(self, name, q):
   """ Calculates the transform for a joint or link
   name string: name of the joint or link, or end-effector
   q np.array: joint angles
   """
   # check for function in dictionary
   if self._J.get(name, None) is None:
        print('Generating Jacobian function for %s'%name)
        self._J[name] = self.calc_J(name)
   return np.array(self._J[name](*q)).T

So how you use this class (all of this is in a class) is to call these T and J functions with the current joint angles. They’ll check to see if the functions have already be created or stored in file, if they haven’t then the T and / or J functions will be created, then our wrappers do a bit of formatting to get them into the proper form (i.e. transposing or cropping), and return you your (x,y,z) or Jacobian!

NOTE: It’s a bit of a misnomer to have the function be called T and actually return to you Tx, but hey this is a blog. Lay off.

Calculating the inertia matrix in joint-space and force of gravity
Now, since we’re here we might as well also calculate the functions for our inertia matrix in joint space and the effect of gravity. So, define a couple more placeholders in our robot_config class to help us:

self._M = []  # placeholder for (x,y,z) inertia matrices
self._Mq = None  # placeholder for joint space inertia matrix function
self._Mq_g = None  # placeholder for joint space gravity term function

and then add in our inertia matrix information (defined in each link’s centre-of-mass (COM) reference frame)

# create the inertia matrices for each link of the ur5
self._M.append(np.diag([1.0, 1.0, 1.0,
                        0.02, 0.02, 0.02]))  # link0
self._M.append(np.diag([2.5, 2.5, 2.5,
                        0.04, 0.04, 0.04]))  # link1
self._M.append(np.diag([5.7, 5.7, 5.7,
                        0.06, 0.06, 0.04]))  # link2
self._M.append(np.diag([3.9, 3.9, 3.9,
                        0.055, 0.055, 0.04]))  # link3
self._M.append(np.copy(self._M[1]))  # link4
self._M.append(np.copy(self._M[1]))  # link5
self._M.append(np.diag([0.7, 0.7, 0.7,
                        0.01, 0.01, 0.01]))  # link6

and then using our equations for calculating the system’s inertia and gravity we create our _calc_Mq and _calc_Mq_g functions

def _calc_Mq(self, lambdify=True):
    """ Uses Sympy to generate the inertia matrix in
    joint space for the ur5

    lambdify boolean: if True returns a function to calculate
                      the Jacobian. If False returns the Sympy
                      matrix
    """

    # check to see if we have our inertia matrix saved in file
    if os.path.isfile('%s/Mq' % self.config_folder):
        Mq = cloudpickle.load(open('%s/Mq' % self.config_folder, 'rb'))
    else:
        # get the Jacobians for each link's COM
        J = [self._calc_J('link%s' % ii, lambdify=False)
             for ii in range(self.num_links)]

        # transform each inertia matrix into joint space
        # sum together the effects of arm segments' inertia on each motor
        Mq = sp.zeros(self.num_joints)
        for ii in range(self.num_links):
            Mq += J[ii].T * self._M[ii] * J[ii]

        # save to file
        cloudpickle.dump(Mq, open('%s/Mq' % self.config_folder, 'wb'))

    if lambdify is False:
        return Mq
    return sp.lambdify(self.q, Mq)

def _calc_Mq_g(self, lambdify=True):
    """ Uses Sympy to generate the force of gravity in
    joint space for the ur5

    lambdify boolean: if True returns a function to calculate
                      the Jacobian. If False returns the Sympy
                      matrix
    """

    # check to see if we have our gravity term saved in file
    if os.path.isfile('%s/Mq_g' % self.config_folder):
        Mq_g = cloudpickle.load(open('%s/Mq_g' % self.config_folder,
                                     'rb'))
    else:
        # get the Jacobians for each link's COM
        J = [self._calc_J('link%s' % ii, lambdify=False)
             for ii in range(self.num_links)]

        # transform each inertia matrix into joint space and
        # sum together the effects of arm segments' inertia on each motor
        Mq_g = sp.zeros(self.num_joints, 1)
        for ii in range(self.num_joints):
            Mq_g += J[ii].T * self._M[ii] * self.gravity

        # save to file
        cloudpickle.dump(Mq_g, open('%s/Mq_g' % self.config_folder,
                                    'wb'))

    if lambdify is False:
        return Mq_g
    return sp.lambdify(self.q, Mq_g)

and wrapper functions

def Mq(self, q):
    """ Calculates the joint space inertia matrix for the ur5

    q np.array: joint angles
    """
    # check for function in dictionary
    if self._Mq is None:
        print('Generating inertia matrix function')
        self._Mq = self._calc_Mq()
    return np.array(self._Mq(*q))

def Mq_g(self, q):
    """ Calculates the force of gravity in joint space for the ur5

    q np.array: joint angles
    """
    # check for function in dictionary
    if self._Mq_g is None:
        print('Generating gravity effects function')
        self._Mq_g = self._calc_Mq_g()
    return np.array(self._Mq_g(*q)).flatten()

and we’re all set!

Putting it all together

Now we have nice clean code to generate everything we need for our controller. Using the controller developed in this post as a base, we can replace those calculations with the following nice compact code (which also includes a secondary null-space controller to keep the arm near resting joint angles):

# calculate position of the end-effector
# derived in the ur5 calc_TnJ class
xyz = robot_config.T('EE', q)
# calculate the Jacobian for the end effector
JEE = robot_config.J('EE', q)
# calculate the inertia matrix in joint space
Mq = robot_config.Mq(q)
# calculate the effect of gravity in joint space
Mq_g = robot_config.Mq_g(q)

# convert the mass compensation into end effector space
Mx_inv = np.dot(JEE, np.dot(np.linalg.inv(Mq), JEE.T))
svd_u, svd_s, svd_v = np.linalg.svd(Mx_inv)
# cut off any singular values that could cause control problems
singularity_thresh = .00025
for i in range(len(svd_s)):
    svd_s[i] = 0 if svd_s[i] < singularity_thresh else \
        1./float(svd_s[i])
# numpy returns U,S,V.T, so have to transpose both here
Mx = np.dot(svd_v.T, np.dot(np.diag(svd_s), svd_u.T))

kp = 100
kv = np.sqrt(kp)
# calculate desired force in (x,y,z) space
u_xyz = np.dot(Mx, target_xyz - xyz)
# transform into joint space, add vel and gravity compensation
u = (kp * np.dot(JEE.T, u_xyz) - np.dot(Mq, kv * dq) - Mq_g)

# calculate our secondary control signal
# calculated desired joint angle acceleration
q_des = (((robot_config.rest_angles - q) + np.pi) %
         (np.pi*2) - np.pi)
u_null = np.dot(Mq, (kp * q_des - kv * dq))

# calculate the null space filter
Jdyn_inv = np.dot(Mx, np.dot(JEE, np.linalg.inv(Mq)))
null_filter = (np.eye(robot_config.num_joints) -
               np.dot(JEE.T, Jdyn_inv))
u_null_filtered = np.dot(null_filter, u_null)

u += u_null_filtered

And there you go!

You can see all of this code up on my GitHub, along a full example controlling a UR5 VREP model though reaching to a series of targets. It looks something pretty much like this (slightly better because this gif was made before adding in the null space controller):

ur5.gif

Overhead of using lambdify instead of hard-coding

This was a big question that I had, because when I’m running simulations the time step is on the order of a few milliseconds, with the controller code called at every time step. So I reaaaally can’t afford a crazy overhead for using lambdify.

To test this, I used the handy Python timeit, which requires a bit awkward setup, but quite nicely calls the function a whole bunch of times (1,000,000 by default) and accounts for various other things going on that could affect the execution time.

I tested two sample functions, one simpler than the other. Here’s the code for setting up and testing the first function:

import timeit
import seaborn

# Test 1 ----------------------------------------------------------------------
print('\nTest function 1: ')
time_sympy1 = timeit.timeit(
        stmt = 'f(np.random.random(), np.random.random())',
        setup = 'import numpy as np;\
                import sympy as sp;\
                q0 = sp.Symbol("q0");\
                l0 = sp.Symbol("l0");\
                a = sp.cos(q0) * l0;\
                f = sp.lambdify((q0, l0), a, "numpy")')
print('Sympy lambdify function 1 time: ', time_sympy1)

time_hardcoded1 = timeit.timeit(
        stmt = 'np.cos(np.random.random())*np.random.random()',
        setup = 'import numpy as np')
print('Hard coded function 1 time: ', time_hardcoded1)

Pretty simple, a bit of a pain in the sympy setup, but other than that not bad at all. The second function I tested was just a random collection of cos and sin calls that resemble what gets computed in a Jacobian:

l1*np.sin(q0 - l0*np.sin(q1)*np.cos(q2) - l2*np.sin(q2) - l0*np.sin(q1) + q0*l0)*np.cos(q0) + l2*np.sin(q0)

And here’s the results:

simtime

So it’s slower for sure, but again this is the difference in time after 1,000,000 function calls, so until some big optimization needs to be done using the SymPy lambdify function is definitely worth the slight gain in execution time for the insane convenience.

The full code for the timing tests here are also up on my GitHub.

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