Optimal Motion Generation-tools is a software toolbox that facilitates the modeling, simulation and embedding of motion planning problems. Its main goal is to bring several research topics concerning (spline-based) motion planning together into a user-friendly package in order to enlarge its visibility towards the scientific and industrial world.

Motion planning approaches implemented in OMG-tools are described in the following publications:

• T. Mercy, W. Van Loock and G. Pipeleers. "Real-time motion planning in the presence of moving obstacles", accepted for the European Control Conference (ECC), 2016.
• R. Van Parys and G. Pipeleers. "Online distributed motion-planning for multi-vehicle systems", accepted for the European Control Conference (ECC), 2016.

If these approaches help you with your research, please cite us!

Any questions can be addressed to the developers: ruben[dot]vanparys[at]kuleuven[dot]be and tim[dot]mercy[at]kuleuven[dot]be.

OMG-tools is written in Python and uses CasADi as a framework for symbolic computations. Furthermore CasADi provides an interface to IPOPT, a software package for large-scale nonlinear optimization. For installation instructions regarding these software packages, the user is referred to the CasADi installation page. The current implementation of this toolbox relies on CasADi 2.4. With the toolbox it is possible to save your simulation results in Tikz format. This functionality uses the matplotlib2tikz script. To install the toolbox itself, run the following command in the root directory of this repository: sudo python setup.py install

The code example below illustrates the basic functionality of the toolbox for steering a holonomic vehicle from an initial to terminal pose in an obstructed environment.

from omgtools import *

# make and set-up vehicle
vehicle = Holonomic()
vehicle.set_initial_conditions([-1.5, -1.5])
vehicle.set_terminal_conditions([2., 2.])
vehicle.set_options({'safety_distance': 0.1})

# make and set-up environment
environment = Environment(room={'shape': Square(5.)})

# add stationary obstacles to environment
rectangle = Rectangle(width=3., height=0.2)
environment.add_obstacle(Obstacle({'position': [-2.1, -0.5]}, shape=rectangle))
environment.add_obstacle(Obstacle({'position': [ 1.7, -0.5]}, shape=rectangle))

# generate trajectory for moving obstacle
traj = {'velocity': {3.: [-0.15, 0.0], 4.: [0., 0.15]}}
# add moving obstacle to environment
environment.add_obstacle(Obstacle({'position': [1.5, 0.5]}, shape=Circle(0.4),trajectories=traj))

# give problem settings and create problem
problem = Point2point(vehicle, environment)
problem.init()

# simulate, plot some signals and save a movie
simulator = Simulator(problem)
simulator.plot.show('input', label=['x-velocity(m/s)', 'y-velocity(m/s)'])
simulator.plot.show('scene')
simulator.run()
simulator.plot.save_movie('scene')

Check out the examples directory for more advanced code examples. There you can find a simple tutorial example which provides a documented overview of the basic functionality of the toolbox. Also examples illustrating distributed motion planning approaches for multi-vehicle systems are available.