{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Kinematic chain in a plane (2D)\n", "\n", "> Marcos Duarte, Renato Naville Watanabe \n", "> [Laboratory of Biomechanics and Motor Control](http://pesquisa.ufabc.edu.br/bmclab) \n", "> Federal University of ABC, Brazil" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

# Contents

\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Kinematic chain refers to an assembly of rigid bodies (links) connected by joints that is the mathematical model for a mechanical system which in turn can represent a biological system such as the human arm ([Wikipedia](http://en.wikipedia.org/wiki/Kinematic_chain)). The term chain refers to the fact that the links are constrained by their connections (typically, by a hinge joint which is also called pin joint or revolute joint) to other links. As consequence of this constraint, a kinematic chain in a plane is an example of circular motion of a rigid object. \n", "\n", "Chapter 16 of Ruina and Rudra's book is a good formal introduction on the topic of circular motion of a rigid object. However, in this notebook we will not employ the mathematical formalism introduced in that chapter - the concept of a rotating reference frame and the related rotation matrix - we cover these subjects in the notebooks [Time-varying frame of reference](http://nbviewer.jupyter.org/github/BMClab/BMC/blob/master/notebooks/Time-varying%20frames.ipynb) and [Rigid-body transformations (2D)](https://nbviewer.jupyter.org/github/BMClab/BMC/blob/master/notebooks/Transformation2D.ipynb). Now, we will describe the kinematics of a chain in a Cartesian coordinate system using trigonometry and calculus. This approach is simpler and more intuitive but it gets too complicated for a kinematic chain with many links or in the 3D space. For such more complicated problems, it would be recommended using rigid transformations (see for example, Siciliano et al. (2009)). \n", "\n", "We will deduce the kinematic properties of kinematic chains algebraically using [Sympy](http://sympy.org/), a Python library for symbolic mathematics. And in Sympy we could have used the [mechanics module](http://docs.sympy.org/latest/modules/physics/mechanics/index.html), a specific module for creation of symbolic equations of motion for multibody systems, but let's deduce most of the stuff by ourselves to understand the details." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "## Properties of kinematic chains\n", "\n", "For a kinematic chain, the base is the extremity (origin) of a kinematic chain which is typically considered attached to the ground, body or fixed. The endpoint is the other extremity (end) of a kinematic chain and typically can move. In robotics, the term end-effector is used and usually refers to a last link (rigid body) in this chain. \n", "\n", "In topological terms, a kinematic chain is termed open when there is only one sequence of links connecting the two ends of the chain. Otherwise it's termed closed and in this case a sequence of links forms a loop. A kinematic chain can be classified as serial or parallel or a mixed of both. In a serial chain the links are connected in a serial order. A serial chain is an open chain, otherwise it is a parallel chain or a branched chain (e.g., hand and fingers). \n", "\n", "Although the definition above is clear and classic in mechanics, it is unfortunately not the definition used by health professionals (clinicians and athletic trainers) when describing human movement. They refer to human joints and segments as a closed or open kinematic (or kinetic) chain simply if the distal segment (typically the foot or hand) is fixed (closed chain) or not (open chain). \n", "In this text we will be consistent with mechanics, but keep in mind this difference when interacting with clinicians and athletic trainers.\n", "\n", "Another important term to characterize a kinematic chain is degree of freedom (DOF). In mechanics, the degree of freedom of a mechanical system is the number of independent parameters that define its configuration or that determine the state of a physical system. A particle in the 3D space has three DOFs because we need three coordinates to specify its position. A rigid body in the 3D space has six DOFs because we need three coordinates of one point at the body to specify its position and three angles to to specify its orientation in order to completely define the configuration of the rigid body. For a link attached to a fixed body by a hinge joint in a plane, all we need to define the configuration of the link is one angle and then this link has only one DOF. A kinematic chain with two links in a plane has two DOFs, and so on.\n", "\n", "The mobility of a kinematic chain is its total number of degrees of freedom. The redundancy of a kinematic chain is its mobility minus the number of degrees of freedom of the endpoint." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## The kinematics of one-link system\n", "\n", "First, let's study the case of a system composed by one planar hinge joint and one link, which technically it's not a chain but it will be useful to review (or introduce) key concepts. \n", "
\n", "\n", "\n", "First, let's import the necessary libraries from Python and its ecosystem:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:00:47.744805Z", "start_time": "2020-11-03T23:00:46.900135Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "sns.set_context(\"notebook\", font_scale=1.2, rc={\"lines.linewidth\": 2,\n", " \"lines.markersize\": 10})\n", "from IPython.display import display, Math\n", "\n", "from sympy import Symbol, symbols, Function\n", "from sympy import Matrix, simplify, lambdify, expand, latex\n", "from sympy import diff, cos, sin, sqrt, acos, atan2, atan, Abs\n", "from sympy.vector import CoordSys3D\n", "from sympy.physics.mechanics import dynamicsymbols, mlatex, init_vprinting\n", "init_vprinting()\n", "\n", "import sys\n", "sys.path.insert(1, r'./../functions') # add to pythonpath" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We need to define a Cartesian coordinate system and the symbolic variables, $t$, $\\ell$, $\\theta$ (and make $\\theta$ a function of time):" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:05:42.962524Z", "start_time": "2020-11-03T23:05:42.959723Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "G = CoordSys3D('')\n", "t = Symbol('t')\n", "l = Symbol('ell', real=True, positive=True)\n", "# type \\theta and press tab for the Greek letter θ\n", "θ = dynamicsymbols('theta', real=True) # or Function('theta')(t) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Using trigonometry, the endpoint position in terms of the joint angle and link length is:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:05:46.157963Z", "start_time": "2020-11-03T23:05:46.023862Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$\\displaystyle (\\ell \\cos{\\left(\\theta{\\left(t \\right)} \\right)})\\mathbf{\\hat{i}_{}} + (\\ell \\sin{\\left(\\theta{\\left(t \\right)} \\right)})\\mathbf{\\hat{j}_{}}$" ], "text/plain": [ "(ell*cos(theta(t)))*.i + (ell*sin(theta(t)))*.j" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r_p = l*cos(θ)*G.i + l*sin(θ)*G.j + 0*G.k\n", "r_p" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "With the components:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:06:09.846336Z", "start_time": "2020-11-03T23:06:09.709499Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$\\displaystyle \\left\\{ \\mathbf{\\hat{i}_{}} : \\ell \\operatorname{cos}\\left(\\theta\\right), \\ \\mathbf{\\hat{j}_{}} : \\ell \\operatorname{sin}\\left(\\theta\\right)\\right\\}$" ], "text/plain": [ "{i_: ell⋅cos(θ), j_: ell⋅sin(θ)}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r_p.components" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Forward and inverse kinematics\n", "\n", "Computing the configuration of a link or a chain (including the endpoint location) from the joint parameters (joint angles and link lengths) as we have done is called [forward or direct kinematics](https://en.wikipedia.org/wiki/Forward_kinematics).\n", "\n", "If the linear coordinates of the endpoint position are known (for example, if they are measured with a motion capture system) and one wants to obtain the joint angle(s), this process is known as [inverse kinematics](https://en.wikipedia.org/wiki/Inverse_kinematics). For the one-link system above:\n", "\n", " \n", "$$\\theta = arctan\\left(\\frac{y_P}{x_P}\\right)$$\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Matrix representation of the kinematics\n", "\n", "The mathematical manipulation will be easier if we use the matrix formalism (and let's drop the explicit dependence on $t$):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:15.472258Z", "start_time": "2020-11-03T23:27:15.339992Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$\\displaystyle \\left[\\begin{matrix}\\ell \\operatorname{cos}\\left(\\theta\\right)\\\\\\ell \\operatorname{sin}\\left(\\theta\\right)\\end{matrix}\\right]$" ], "text/plain": [ "⎡ell⋅cos(θ)⎤\n", "⎢ ⎥\n", "⎣ell⋅sin(θ)⎦" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r = Matrix((r_p.dot(G.i), r_p.dot(G.j)))\n", "r" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the matrix formalism will simplify things, but we will loose some of the Sympy methods for vectors (for instance, the variable r_p has a method magnitude and the variable r does not. \n", "If you prefer, you can keep the pure vector representation and just switch to matrix representation when displaying a variable:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:17.260232Z", "start_time": "2020-11-03T23:27:17.126539Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { 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"text/latex": [ "$\\displaystyle \\left[\\begin{matrix}\\ell \\operatorname{cos}\\left(\\theta\\right)\\\\\\ell \\operatorname{sin}\\left(\\theta\\right)\\\\0\\end{matrix}\\right]$" ], "text/plain": [ "⎡ell⋅cos(θ)⎤\n", "⎢ ⎥\n", "⎢ell⋅sin(θ)⎥\n", "⎢ ⎥\n", "⎣ 0 ⎦" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r_p.to_matrix(G)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The third element of the matrix above refers to the $\\hat{\\mathbf{k}}$ component which is zero for the present case (planar movement). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Differential kinematics\n", "\n", "Differential kinematics gives the relationship between the joint velocities and the corresponding endpoint linear velocity. This mapping is described by a matrix, termed [Jacobian matrix](http://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant), which depends on the kinematic chain configuration and it is of great use in the study of kinematic chains. \n", "First, let's deduce the endpoint velocity without using the Jacobian and then we will see how to calculate the endpoint velocity using the Jacobian matrix.\n", "\n", "The velocity of the endpoint can be obtained by the first-order derivative of the position vector. The derivative of a vector is obtained by differentiating each vector component: \n", "\n", "\n", "$$\n", "\\frac{\\mathrm{d}\\overrightarrow{\\mathbf{r}}}{\\mathrm{d}t} = \n", "\\large\n", "\\begin{bmatrix}\n", "\\frac{\\mathrm{d}x_P}{\\mathrm{d}t} \\\\\n", "\\frac{\\mathrm{d}y_P}{\\mathrm{d}t} \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "\n", "Note that the derivative is with respect to time but $x_P$ and $y_P$ depend explicitly on $\\theta$ and it's $\\theta$ that depends on $t$ ($x_P$ and $y_P$ depend implicitly on $t$). To calculate this type of derivative we will use the [chain rule](http://en.wikipedia.org/wiki/Chain_rule). \n", "\n", "
\n", "
\n", "Chain rule \n", "
\n", "For variable $f$ which is function of variable $g$ which in turn is function of variable $t$, $f(g(t))$ or $(f\\circ g)(t)$, the derivative of $f$ with respect to $t$ is (using Lagrange's notation): \n", "
\n", "\n", "$$(f\\circ g)^{'}(t) = f'(g(t)) \\cdot g'(t)$$ \n", "\n", " \n", "Or using what is known as Leibniz's notation: \n", "
\n", "\n", "$$\\frac{\\mathrm{d}f}{\\mathrm{d}t} = \\frac{\\mathrm{d}f}{\\mathrm{d}g} \\cdot \\frac{\\mathrm{d}g}{\\mathrm{d}t}$$ \n", "\n", "\n", "If $f$ is function of two other variables which both are function of $t$, $f(x(t),y(t))$, the chain rule for this case is: \n", "
\n", "\n", "$$\\frac{\\mathrm{d}f}{\\mathrm{d}t} = \\frac{\\partial f}{\\partial x} \\cdot \\frac{\\mathrm{d}x}{\\mathrm{d}t} + \\frac{\\partial f}{\\partial y} \\cdot \\frac{\\mathrm{d}y}{\\mathrm{d}t}$$ \n", "\n", " \n", "Where $df/dt$ represents the total derivative and $\\partial f / \\partial x$ represents the partial derivative of a function. \n", "
\n", "Product rule \n", "
\n", "The derivative of the product of two functions is: \n", "
\n", "\n", "$$(f \\cdot g)' = f' \\cdot g + f \\cdot g'$$\n", "\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Linear velocity of the endpoint\n", "\n", "For the planar one-link case, the linear velocity of the endpoint is:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:19.395714Z", "start_time": "2020-11-03T23:27:19.251844Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$\\displaystyle \\left[\\begin{matrix}- \\ell \\operatorname{sin}\\left(\\theta\\right) \\dot{\\theta}\\\\\\ell \\operatorname{cos}\\left(\\theta\\right) \\dot{\\theta}\\end{matrix}\\right]$" ], "text/plain": [ "⎡-ell⋅sin(θ)⋅θ̇⎤\n", "⎢ ⎥\n", "⎣ell⋅cos(θ)⋅θ̇ ⎦" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v = r.diff(t)\n", "v" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Where we used the [Newton's notation](http://en.wikipedia.org/wiki/Notation_for_differentiation) for differentiation. \n", "Note that $\\dot{\\theta}$ represents the unknown angular velocity of the joint; this is why the derivative of $\\theta$ is not explicitly solved. \n", "The magnitude or [Euclidian norm](http://en.wikipedia.org/wiki/Vector_norm) of the vector $\\overrightarrow{\\mathbf{v}}$ is:\n", "\n", "\n", "$$||\\overrightarrow{\\mathbf{v}}||=\\sqrt{v_x^2+v_y^2}$$\n", "" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:20.530788Z", "start_time": "2020-11-03T23:27:20.332885Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$\\displaystyle \\ell \\sqrt{\\dot{\\theta}^{2}}$" ], "text/plain": [ " ____\n", " ╱ 2 \n", "ell⋅╲╱ θ̇ " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(sqrt(v[0]**2 + v[1]**2))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Which is $\\ell\\dot{\\theta}$.
\n", "We could have used the function norm of Sympy, but the output does not simplify nicely:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:21.463355Z", "start_time": "2020-11-03T23:27:21.201932Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$\\displaystyle \\ell \\sqrt{\\left|{\\operatorname{sin}\\left(\\theta\\right) \\dot{\\theta}}\\right|^{2} + \\left|{\\operatorname{cos}\\left(\\theta\\right) \\dot{\\theta}}\\right|^{2}}$" ], "text/plain": [ " ___________________________\n", " ╱ 2 2 \n", "ell⋅╲╱ │sin(θ)⋅θ̇│ + │cos(θ)⋅θ̇│ " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(v.norm())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The direction of $\\overrightarrow{\\mathbf{v}}$ is tangent to the circular trajectory of the endpoint as can be seen in the figure below where its components are also shown.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Linear acceleration of the endpoint\n", "\n", "The acceleration of the endpoint position can be given by the second-order derivative of the position or by the first-order derivative of the velocity. \n", "Using the chain and product rules for differentiation, the linear acceleration of the endpoint is:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:23.185455Z", "start_time": "2020-11-03T23:27:23.039800Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$\\displaystyle \\left[\\begin{matrix}- \\ell \\operatorname{sin}\\left(\\theta\\right) \\ddot{\\theta} - \\ell \\operatorname{cos}\\left(\\theta\\right) \\dot{\\theta}^{2}\\\\- \\ell \\operatorname{sin}\\left(\\theta\\right) \\dot{\\theta}^{2} + \\ell \\operatorname{cos}\\left(\\theta\\right) \\ddot{\\theta}\\end{matrix}\\right]$" ], "text/plain": [ "⎡ 2 ⎤\n", "⎢-ell⋅sin(θ)⋅θ̈ - ell⋅cos(θ)⋅θ̇ ⎥\n", "⎢ ⎥\n", "⎢ 2 ⎥\n", "⎣- ell⋅sin(θ)⋅θ̇ + ell⋅cos(θ)⋅θ̈⎦" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acc = v.diff(t, 1)\n", "acc" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Examining the terms of the expression for the linear acceleration, we see there are two types of them: the term (in each direction) proportional to the angular acceleration $\\ddot{\\theta}$ and other term proportional to the square of the angular velocity $\\dot{\\theta}^{2}$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Tangential acceleration\n", "\n", "The term proportional to angular acceleration, $a_t$, is always tangent to the trajectory of the endpoint (see figure below) and it's magnitude or Euclidean norm is:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:24.758567Z", "start_time": "2020-11-03T23:27:24.588832Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$\\displaystyle \\ell \\ddot{\\theta}$" ], "text/plain": [ "ell⋅θ̈" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = θ.diff(t, 2)\n", "simplify(sqrt(expand(acc[0]).coeff(A)**2 + expand(acc[1]).coeff(A)**2))*A" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### Centripetal acceleration\n", "\n", "The term proportional to angular velocity, $a_c$, always points to the joint, the center of the circular motion (see figure below), because of that this term is termed [centripetal acceleration](http://en.wikipedia.org/wiki/Centripetal_acceleration#Tangential_and_centripetal_acceleration). Its magnitude is:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:26.883182Z", "start_time": "2020-11-03T23:27:26.732230Z" }, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": "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\n", "text/latex": [ "$\\displaystyle \\ell \\dot{\\theta}^{2}$" ], "text/plain": [ " 2\n", "ell⋅θ̇ " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = θ.diff(t)**2\n", "simplify(sqrt(expand(acc[0]).coeff(A)**2+expand(acc[1]).coeff(A)**2))*A" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "This means that there will be a linear acceleration even if the angular acceleration is zero because although the magnitude of the linear velocity is constant in this case, its direction varies (due to the centripetal acceleration). \n", "
\n", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Let's plot some simulated data to have an idea of the one-link kinematics. \n", "Consider $\\ell=1\\:m,\\theta_i=0^o,\\theta_f=90^o$, and $1\\:s$ of movement duration, and that it is a [minimum-jerk movement](https://nbviewer.jupyter.org/github/BMClab/bmc/blob/master/notebooks/MinimumJerkHypothesis.ipynb)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:28.623718Z", "start_time": "2020-11-03T23:27:28.565060Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "θ_i, θ_f, d = 0, np.pi/2, 1\n", "ts = np.arange(0.01, 1.01, .01)\n", "mjt = θ_i + (θ_f - θ_i)*(10*(t/d)**3 - 15*(t/d)**4 + 6*(t/d)**5)\n", "\n", "ang = lambdify(t, mjt, 'numpy'); ang = ang(ts)\n", "vang = lambdify(t, mjt.diff(t,1), 'numpy'); vang = vang(ts)\n", "aang = lambdify(t, mjt.diff(t,2), 'numpy'); aang = aang(ts)\n", "jang = lambdify(t, mjt.diff(t,3), 'numpy'); jang = jang(ts)\n", "\n", "b, c, d, e = symbols('b c d e')\n", "dicti = {l:1, θ:b, θ.diff(t, 1):c, θ.diff(t, 2):d, θ.diff(t, 3):e}\n", "\n", "r2 = r.subs(dicti);\n", "rxfu = lambdify(b, r2[0], modules = 'numpy')\n", "ryfu = lambdify(b, r2[1], modules = 'numpy')\n", "\n", "v2 = v.subs(dicti);\n", "vxfu = lambdify((b, c), v2[0], modules = 'numpy')\n", "vyfu = lambdify((b, c), v2[1], modules = 'numpy')\n", "\n", "acc2 = acc.subs(dicti);\n", "axfu = lambdify((b, c, d), acc2[0], modules = 'numpy')\n", "ayfu = lambdify((b, c, d), acc2[1], modules = 'numpy')\n", "\n", "jerk = r.diff(t,3)\n", "jerk2 = jerk.subs(dicti);\n", "jxfu = lambdify((b, c, d, e), jerk2[0], modules = 'numpy')\n", "jyfu = lambdify((b, c, d, e), jerk2[1], modules = 'numpy')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2020-11-03T23:27:30.251177Z", "start_time": "2020-11-03T23:27:29.584054Z" }, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "image/png": 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\n", 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