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ch.ethz.idsc.owl Build Status

Library for motion planning in Java, version 0.5.9

owl

The library was developed with the following objectives in mind

  • suitable for use in safety-critical real-time systems
  • trajectory planning for an autonomous vehicle
  • implementation of theoretical concepts with high level of abstraction
  • simulation and visualization

usecase_motionplan

Motion planning

shadow_regions

Obstacle anticipation

usecase_gokart

Trajectory pursuit

planning_obstacles

Static obstacles

Student Projects

2017

  • Jonas Londschien (MT): An Anytime Generalized Label Correcting Method for Motion Planning

2018

  • Yannik Nager (MT): What lies in the shadows? Safe and computation-aware motion planning for autonomous vehicles using intent-aware dynamic shadow regions

2019

  • André Stoll (MT): Multi-Objective Optimization Using Preference Structures
  • Oliver Brinkmann (MT): Averaging on Lie Groups: Applications of Geodesic Averages and Biinvariant Means
  • Joel Gächter (MT): Subdivision-Based Clothoids in Autonomous Driving

Features

  • Motion planning algorithms: GLC, and RRT*
  • integrators: Euler, Midpoint, Runge-Kutta 4-5th order, exact integrator for the group SE2
  • state-space models: car-like, two-wheel-drive, pendulum-swing-up, Lotka-Volterra, etc.
  • efficient heuristic for goal regions: sphere, conic section
  • visualizations and animations, see video

Motion Planning

GLC

Rice2: 4-dimensional state space + time

rice2dentity_1510227502495

rice2dentity_1510234462100


SE2: 3-dimensional state space

Car

se2entity_1510232282788

Two-wheel drive (with Lidar simulator)

twdentity_1510751358909


Simulation: autonomous gokart or car

Gokart

_1530775215911

Car

_1530775403211

RRT*

R^2

r2ani

r2

Nearest Neighbors

nearest_r2

R^2

nearest_dubins

Dubins

nearest_clothoid

Clothoid

Integration

Specify repository and dependency of the owl library in the pom.xml file of your maven project:

<repositories>
  <repository>
    <id>owl-mvn-repo</id>
    <url>https://raw.github.com/idsc-frazzoli/owl/mvn-repo/</url>
    <snapshots>
      <enabled>true</enabled>
      <updatePolicy>always</updatePolicy>
    </snapshots>
  </repository>
</repositories>

<dependencies>
  <dependency>
    <groupId>ch.ethz.idsc</groupId>
    <artifactId>owl</artifactId>
    <version>0.5.9</version>
  </dependency>
</dependencies>

Contributors

Jan Hakenberg, Jonas Londschien, Yannik Nager, André Stoll, Joel Gaechter

The code in the repository operates a heavy and fast robot that may endanger living creatures. We follow best practices and coding standards to protect from avoidable errors.

Publications

  • What lies in the shadows? Safe and computation-aware motion planning for autonomous vehicles using intent-aware dynamic shadow regions by Yannik Nager, Andrea Censi, and Emilio Frazzoli, video

References

  • A Generalized Label Correcting Method for Optimal Kinodynamic Motion Planning by Brian Paden and Emilio Frazzoli, arXiv:1607.06966, video
  • Sampling-based algorithms for optimal motion planning by Sertac Karaman and Emilio Frazzoli, IJRR11

ethz300

ch.ethz.idsc.sophus Build Status

Library for non-linear geometry computation in Java

sophus

The library was developed with the following objectives in mind

  • trajectory design for autonomous robots
  • suitable for use in safety-critical real-time systems
  • implementation of theoretical concepts with high level of abstraction

curve_se2

Curve Subdivision

smoothing

Smoothing

wachspress

Wachspress

dubinspathcurvature

Dubins path curvature

Features

  • geodesics in Lie-groups and homogeneous spaces: Euclidean space R^n, special Euclidean group SE(2), hyperbolic half-plane H2, n-dimensional sphere S^n, ...
  • parametric curves defined by control points in non-linear spaces: GeodesicBSplineFunction, ...
  • non-linear smoothing of noisy localization data GeodesicCenterFilter
  • Dubins path

Geodesic DeBoor Algorithm

loops5

B-Spline curves in SE(2) produced by DeBoor Algorithm or curve subdivision produce curves in the planar subspace R^2 with appealing curvature.

Smoothing using Geodesic Averages

smoothing

The sequence of localization estimates of a mobile robot often contains noise. Instead of using a complicated extended Kalman filter, geodesic averages based on conventional window functions denoise the uniformly sampled signal of poses in SE(2).

Curve Decimation in Lie Groups

curve_decimation

The pose of mobile robots is typically recorded at high frequencies. The trajectory can be faithfully reconstructed from a fraction of the samples.

Visualization of Geodesic Averages

deboor5

A geodesic average is the generalization of an affine combination from the Euclidean space to a non-linear space. A geodesic average consists of a nested binary averages. Generally, an affine combination does not have a unique expression as a geodesic average. Instead, several geodesic averages reduce to the same affine combination when applied in Euclidean space.

Contributors

Jan Hakenberg, Oliver Brinkmann, Joel Gächter

Publications

References

  • Bi-invariant Means in Lie Groups. Application to Left-invariant Polyaffine Transformations. by Vincent Arsigny, Xavier Pennec, Nicholas Ayache
  • Exponential Barycenters of the Canonical Cartan Connection and Invariant Means on Lie Groups by Xavier Pennec, Vincent Arsigny
  • Lie Groups for 2D and 3D Transformations by Ethan Eade
  • Manifold-valued subdivision schemes based on geodesic inductive averaging by Nira Dyn, Nir Sharon
  • Power Coordinates: A Geometric Construction of Barycentric Coordinates on Convex Polytopes by Max Budninskiy, Beibei Liu, Yiying Tong, Mathieu Desbrun

ethz300


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