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Update the introduction part of sphinx doco
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‎docs/source/intro.rst

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@@ -6,7 +6,7 @@ Introduction
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Introduction
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============
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*This is a modified version of a paper submitted to ICRA2020*
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This is a modified version of a paper accepted to ICRA2021 [corke21a]_.
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The Robotics Toolbox for MATLAB® (RTB-M) was created around 1991 to support
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Peter Corke’s PhD research and was first published in 1995-6 [Corke95]_
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.. runblock:: pycon
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:linenos:
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>>> from roboticstoolbox import ETS as E
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>>> from roboticstoolbox import ETS as ET
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>>> import roboticstoolbox as rtb
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>>> l1 = 0.672; l2 = 0.2337; l3 = 0.4318; l4 = -0.0837; l5 = 0.4318; l6 = 0.0203
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>>> e = E.tz(l1) * E.rz() * E.ty(l2) * E.ry() * E.tz(l3) * E.tx(l6) * E.ty(l4) * E.ry() * E.tz(l5) * E.rz() * E.ry() * E.rz()
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>>> # Puma dimensions (m), see RVC2 Fig. 7.4 for details
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>>> l1 = 0.672; l2 = -0.2337; l3 = 0.4318; l4 = 0.0203; l5 = 0.0837; l6 = 0.4318
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>>> e = ET.tz(l1) * ET.rz() * ET.ty(l2) * ET.ry() * ET.tz(l3) * ET.tx(l4) * ET.ty(l5) * ET.ry() * ET.tz(l6) * ET.rz() * ET.ry() * ET.rz()
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>>> print(e)
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>>> robot = rtb.ERobot(e)
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>>> print(robot)
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>>> import roboticstoolbox as rtb
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>>> panda = rtb.models.URDF.Panda()
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>>> print(panda)
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>>> T = panda.fkine(panda.qz, endlink='panda_hand')
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>>> T = panda.fkine(panda.qz, end='panda_hand')
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>>> print(T)
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In the table above we see the end-effectors indicated by @ (determined automatically
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>>> T1 = SE3(0.4, 0.5, 0.2)
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>>> Ts = rtb.tools.trajectory.ctraj(T0, T1, len(t))
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>>> len(Ts)
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>>> sol = puma.ikine_LM(Ts) # array of named tuples
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>>> qt = np.array([x.q for x in sol]) # convert to 2d matrix
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>>> qt.shape
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>>> sol = puma.ikine_LM(Ts) # named tuple of arrays
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>>> sol.q.shape
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At line 9 we see that the resulting trajectory, ``Ts``, is an ``SE3`` instance with 200 values.
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.. runblock:: pycon
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>>> import roboticstoolbox as rtb
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>>> import spatialmath.base as base
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>>> phi, theta, psi = base.sym.symbol('phi, theta, psi')
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>>> phi, theta, psi = base.sym.symbol('φ, ϴ, ψ')
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>>> base.rpy2r(phi, theta, psi)
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The capability extends to forward kinematics
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>>> T = puma.fkine(q)
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>>> T.t[0]
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If we display the value of ``puma`` we see that the :math:`\alpha_j` values are now displayed in red to indicate that they are symbolic constants. The x-coordinate of the end-effector is
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given by line 7.
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If we display the value of ``puma`` we see that the :math:`\alpha_j` values are
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now displayed in red to indicate that they are symbolic constants. The
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x-coordinate of the end-effector is given by line 7.
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SymPy allows any expression to be converted to runnable code in a variety of languages including C, Python and Octave/MATLAB.
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SymPy allows any expression to be converted to LaTeX or a variety of languages
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including C, Python and Octave/MATLAB.
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Differential kinematics
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=======================
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takes 170ms (:math:`113\times` slower) to produce the unsimplified torque
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expressions.
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For all robots there is also an implementation of Featherstone's spatial vector
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method, ``rne_spatial()``, and SMTB-P provides a set of classes for spatial
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For ``ERobot`` subclasses there is also an implementation of Featherstone's spatial vector
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method, ``rne()``, and SMTB-P provides a set of classes for spatial
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velocity, acceleration, momentum, force and inertia.
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GitHub pages. Issues can be reported via GitHub issues or patches submitted as
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pull requests.
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RTB-P, and its dependencies, can be installed simply by::
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RTB-P, and its dependencies, can be installed simply by either of::
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$ pip install roboticstoolbox-python
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$ conda install -c conda-forge roboticstoolbox-python
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which includes basic visualization using matplotlib.
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Options such as ``vpython`` can be used to specify additional dependencies to be installed.
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The Toolbox adopts a "when needed" approach to many dependencies and will only attempt
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Conclusion
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==========
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This article has introduced and demonstrated in tutorial form the principle features of the Robotics
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Toolbox for Python which runs on Mac, Windows and Linux using Python 3.6 or better.
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The code is free and open, and released under the MIT licence.
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It provides many of the essential tools necessary for
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robotic manipulator modelling, simulation and control which is essential for robotics education and research.
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It is familiar yet new, and we hope it will serve the community well for the next 25 years.
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This article has introduced and demonstrated in tutorial form the principle
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features of the Robotics Toolbox for Python which runs on Mac, Windows and Linux
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using Python 3.6 or better. The code is free and open, and released under the
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MIT licence. It provides many of the essential tools necessary for robotic
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manipulator modelling, simulation and control which is essential for robotics
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education and research. It is familiar yet new, and we hope it will serve the
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community well for the next 25 years.
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Currently under development are backend interfaces for CoppeliaSim, Dynamixel
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servo chains, and ROS; symbolic dynamics, simplification and code generation;
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mobile robotics motion models, planners, EKF localization, map making and SLAM;
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and a minimalist block-diagram simulation tool [bdsim]_.
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A high-performance reactive motion controller, NEO, is based on this toolbox
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[neo]_. Currently under development are backend interfaces for CoppeliaSim,
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Dynamixel servo chains, and ROS; symbolic dynamics, simplification and code
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generation; mobile robotics motion models, planners, EKF localization, map
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making and SLAM; and a minimalist block-diagram simulation tool [bdsim]_.
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References
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==========
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.. [Corke95] P. Corke. A computer tool for simulation and analysis: the Robotics Toolbox for MATLAB. In Proc. National Conf. Australian Robot Association, pages 319–330, Melbourne, July 1995.
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.. [Corke96] P. Corke. A robotics toolbox for MATLAB. IEEE Robotics and Automation Magazine, 3(1):24–32, Sept. 1996.
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.. [Craig2005] Introduction to Robotics, John Craig, Wiley, 2005.
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.. [Corke95] `P. Corke. "A computer tool for simulation and analysis: the Robotics Toolbox for MATLAB". In Proc. National Conf. Australian Robot Association, pages 319–330, Melbourne, July 1995. <http://www.petercorke.com/RTB/ARA95.pdf>`_
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.. [Corke96] `P. Corke. "A robotics toolbox for MATLAB". IEEE Robotics and Automation Magazine, 3(1):24–32, Sept. 1996. <https://ieeexplore.ieee.org/document/486658>`_
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.. [Craig2005] J. Craig, "Introduction to Robotics", Wiley, 2005.
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.. [Featherstone87] R. Featherstone, Robot Dynamics Algorithms. Kluwer Academic, 1987.
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.. [Corke07] P. Corke, `“A simple and systematic approach to assigning Denavit- Hartenberg parameters,” IEEE transactions on robotics, vol. 23, no. 3, pp. 590–594, 2007, DOI 10.1109/TRO.2007.896765. <https://ieeexplore.ieee.org/document/4252158>`_.
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.. [Haviland20] `J. Haviland and P. Corke, “A systematic approach to computing the manipulator Jacobian and Hessian using the elementary transform sequence,” arXiv preprint, 2020. <https://arxiv.org/abs/2010.08696>`_
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.. [PyBullet] `PyBullet <https://pybullet.org/wordpress/>`_
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.. [SMTB-P] `Spatial Math Toolbox for Python <https://github.com/petercorke/spatialmath-python>`_
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.. [bdsim] `Block diagram simulator for Python <https://github.com/petercorke/bdsim>`_
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.. [bdsim] `Block diagram simulator for Python <https://github.com/petercorke/bdsim>`_
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.. [neo] `NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulatorshttps://jhavl.github.io/neo>`_
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.. [corke21a] P. Corke and J. Haviland, "Not your grandmother’s toolbox – the Robotics Toolbox reinvented for Python", Proc. ICRA 2021.

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