EXOTica: An Extensible Optimization Toolset for Prototyping and Benchmarking Motion Planning and Control

  • Vladimir IvanEmail author
  • Yiming Yang
  • Wolfgang Merkt
  • Michael P. Camilleri
  • Sethu Vijayakumar
Part of the Studies in Computational Intelligence book series (SCI, volume 778)


In this research chapter, we will present a software toolbox called EXOTica that is aimed at rapidly prototyping and benchmarking algorithms for motion synthesis. We will first introduce the framework and describe the components that make it possible to easily define motion planning problems and implement algorithms that solve them. We will walk you through the existing problem definitions and solvers that we used in our research, and provide you with a starting point for developing your own motion planning solutions. The modular architecture of EXOTica makes it easy to extend and apply to unique problems in research and in industry. Furthermore, it allows us to run extensive benchmarks and create comparisons to support case studies and to generate results for scientific publications. We demonstrate the research done using EXOTica on benchmarking sampling-based motion planning algorithms, using alternate state representations, and integration of EXOTica into a shared autonomy system. EXOTica is an open-source project implemented within ROS and it is continuously integrated and tested with ROS Indigo and Kinetic. The source code is available at and the documentation including tutorials, download and installation instructions are available at


Motion planning Algorithm prototyping Benchmarking Optimization 


  1. 1.
    S. Chitta, I. Sucan, S. Cousins, Moveit! [ros topics]. IEEE Robot. Autom. Mag. (RAM) 19(1), 18–19 (2012)Google Scholar
  2. 2.
    D.V. Lu, M. Ferguson, E. Marder-Eppstein, ROS Navigation Stack,
  3. 3.
    J.-L. Blanco-Claraco, Reactive navigation for 2D robots using MRPT navigation algorithms,
  4. 4.
    B. Siciliano, O. Khatib, Springer Handbook of Robotics (Springer, Berlin, 2008)Google Scholar
  5. 5.
    S.M. LaValle, Planning Algorithms (Cambridge University Press, Cambridge, 2006),
  6. 6.
    M. Toussaint, Robot trajectory optimization using approximate inference, in Proceedings of the 26th Annual International Conference on Machine Learning (ICML) ICML ’09 (ACM, USA, 2009), pp. 1049–1056Google Scholar
  7. 7.
    J.J. Kuffner, S.M. LaValle, Rrt-connect: an efficient approach to single-query path planning, in Proceedings of IEEE International Conference on Robotics and Automation (ICRA) vol. 2 (2000), pp. 995–1001Google Scholar
  8. 8.
    I.A. Şucan, M. Moll, L.E. Kavraki, The open motion planning library. IEEE Robot. Autom. Mag. (RAM) 19(4), 72–82 (2012),
  9. 9.
    A. Hornung, K.M. Wurm, M. Bennewitz, C. Stachniss, W. Burgard, Octomap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots (2013),
  10. 10.
    R. Smits, KDL: Kinematics and Dynamics Library,
  11. 11.
    D. Hershberger, D. Gossow, J. Faust, RViz: 3D visualization tool for ROS,
  12. 12.
    T. Foote, E. Marder-Eppstein, W. Meeussen, TF2: transform library for ROS,
  13. 13.
    J.-L. Blanco, A tutorial on se(3) transformation parameterizations and on-manifold optimization, Technical report, University of Malaga (2010)Google Scholar
  14. 14.
    Y. Nakamura, H. Hanafusa, Inverse kinematic solutions with singularity robustness for robot manipulator control. ASME Trans. J. Dyn. Syst. Meas. Control 108, 163–171 (1986)Google Scholar
  15. 15.
    K. Rawlik, M. Toussaint, S. Vijayakumar, On stochastic optimal control and reinforcement learning by approximate inference (extended abstract), in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI) (AAAI Press, 2013), pp. 3052–3056Google Scholar
  16. 16.
    H. Dai, A. Valenzuela, R. Tedrake, Whole-body motion planning with centroidal dynamics and full kinematics, in Proceedings of IEEE-RAS International Conference on Humanoid Robots (Humanoids) (2014), pp. 295–302Google Scholar
  17. 17.
    S. Bradley, A. Hax, T. Magnanti, Applied Mathematical Programming (Addison-Wesley, USA, 1977)Google Scholar
  18. 18.
    R. Deits, R. Tedrake, Footstep planning on uneven terrain with mixed-integer convex optimization, in Proceedings of IEEE-RAS International Conference on Humanoid Robots (Humanoids) (2014), pp. 279–286Google Scholar
  19. 19.
    B.D. Luders, S. Karaman, J.P. How, Robust sampling-based motion planning with asymptotic optimality guarantees, in Proceedings of the AIAA Guidance, Navigation, and Control (GNC) Conference, American Institute of Aeronautics and Astronautics (2013)Google Scholar
  20. 20.
    M. Hutter, H. Sommer, C. Gehring, M. Hoepflinger, M. Bloesch, R. Siegwart, Quadrupedal locomotion using hierarchical operational space control. Int. J. Robot. Res. (IJRR) 33(8), 1047–1062 (2014)Google Scholar
  21. 21.
    M. Toussaint, A tutorial on Newton methods for constrained trajectory optimization and relations to SLAM, Gaussian Process smoothing, optimal control, and probabilistic inference, in Geometric and Numerical Foundations of Movements, ed. by J.-P. Laumond (Springer, Berlin, 2017)Google Scholar
  22. 22.
    E. Todorov, W. Li, A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems, in Proceedings of the American Control Conference (2005), pp. 300–306Google Scholar
  23. 23.
    Y. Yang, V. Ivan, W. Merkt, and S. Vijayakumar, “Scaling Sampling-based Motion Planning to Humanoid Robots,” in Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1448–1454, 2016Google Scholar
  24. 24.
    V. Ivan, D. Zarubin, M. Toussaint, T. Komura, S. Vijayakumar, Topology-based representations for motion planning and generalization in dynamic environments with interactions. Int. J. Robot. Res. (IJRR) 32, 1151–1163 (2013)Google Scholar
  25. 25.
    Y. Yang, V. Ivan, S. Vijayakumar, Real-time motion adaptation using relative distance space representation, in Proceedings of IEEE International Conference on Advanced Robotics (ICAR) (2015), pp. 21–27Google Scholar
  26. 26.
    V. Ivan, S. Vijayakumar, Space-time area coverage control for robot motion synthesis, in Proceedings of IEEE International Conference on Advanced Robotics (ICAR) 2015, pp. 207–212Google Scholar
  27. 27.
    W. Merkt, Y. Yang, T. Stouraitis, C.E. Mower, M. Fallon, S. Vijayakumar, Robust shared autonomy for mobile manipulation with continuous scene monitoring, in Proceedings of IEEE International Conference on Automation Science and Engineering (CASE) (2017), pp. 130–137Google Scholar
  28. 28.
    Y. Yang, V. Ivan, Z. Li, M. Fallon, S. Vijayakumar, iDRM: humanoid motion planning with realtime end-pose selection in complex environments, in Proceedings of IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) (2016), pp. 271–278Google Scholar
  29. 29.
    Y. Yang, W. Merkt, H. Ferrolho, V. Ivan, S. Vijayakumar, Efficient humanoid motion planning on uneven terrain using paired forward-inverse dynamic reachability maps. IEEE Robot. Autom. Lett. (RA-L) 2(4), 2279–2286 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Vladimir Ivan
    • 1
    Email author
  • Yiming Yang
    • 1
  • Wolfgang Merkt
    • 1
  • Michael P. Camilleri
    • 1
  • Sethu Vijayakumar
    • 1
  1. 1.School of InformaticsThe University of EdinburghEdinburghUK

Personalised recommendations