Whole-Body Planning for Obstacle Traversal with Autonomous Mobile Ground Robots

  • Martin OehlerEmail author
  • Stefan Kohlbrecher
  • Oskar von Stryk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 980)


A common challenge for autonomous mobile ground robots in unstructured environments is the traversal of obstacles without risking to tip over. Previous research on prevention of vehicle tip-over is mostly limited to basic mobility systems with only few degrees of freedom (DOF). In this paper, a novel whole-body motion planning approach is presented. Based on a 3D world model and a given planned path, the trajectories of all joints are optimized to maximize robot stability. The resulting motion plan allows the robot to cross obstacles without tipping over. Compared to existing approaches, the proposed approach considers environment- and self-collisions during planning. Few assumptions about the robot configuration are made which enables the adoption to different mobile platforms. This approach is evaluated for a simulated and a real robot. The platform is a tracked vehicle with adjustable flippers and a five DOF manipulator arm. In several test scenarios, it is shown that the proposed approach effectively prevents tip-over and increases robot stability.


Stability optimization Unstructured environment 


  1. 1.
    Agarwal, S., Mierle, K., et al.: Ceres solver.
  2. 2.
    Beck, C., Miró, J.V., Dissanayake, G.: Trajectory optimisation for increased stability of mobile robots operating in uneven terrains. In: IEEE ICCA, pp. 1913–1919 (2009)Google Scholar
  3. 3.
    Besseron, G., Grand, C., Amar, F.B., Bidaud, P.: Decoupled control of the high mobility robot hylos based on a dynamic stability margin. In: IEEE/RSJ IROS, pp. 2435–2440 (2008)Google Scholar
  4. 4.
    Grand, C., Benamar, F., Plumet, F., Bidaud, P.: Stability and traction optimization of a reconfigurable wheel-legged robot. Int. J. Rob. Res. 23(10–11), 1041–1058 (2004)CrossRefGoogle Scholar
  5. 5.
    Kudruss, M., Manns, P., Kirches, C.: Efficient derivative evaluation for rigid-body dynamics based on recursive algorithms subject to kinematic and loop constraints. Optimization Online Preprint (2019).
  6. 6.
    McGhee, R.B., Frank, A.A.: On the stability properties of quadruped creeping gaits. Math. Biosci. 3, 331–351 (1968)CrossRefGoogle Scholar
  7. 7.
    Messuri, D., Klein, C.: Automatic body regulation for maintaining stability of a legged vehicle during rough-terrain locomotion. IEEE J-RA 1(3), 132–141 (1985)Google Scholar
  8. 8.
    Norouzi, M., Miro, J.V., Dissanayake, G.: Planning stable and efficient paths for reconfigurable robots on uneven terrain. J. Intell. Rob. Syst. 87(2), 291–312 (2017)CrossRefGoogle Scholar
  9. 9.
    Ohno, K., Takeuchi, E., Chun, V., Tadokoro, S., Yuzawa, T., Yoshida, T., Koyanagi, E.: Rollover avoidance using a stability margin for a tracked vehicle with sub-tracks. In: IEEE SSRR, pp. 1–6 (2009)Google Scholar
  10. 10.
    Oleynikova, H., Taylor, Z., Fehr, M., Siegwart, R., Nieto, J.: Voxblox: incremental 3D euclidean signed distance fields for on-board MAV planning. In: IEEE/RSJ IROS (2017)Google Scholar
  11. 11.
    Papadopoulos, E., Rey, D.A.: The force-angle measure of tipover stability margin for mobile manipulators. Veh. Syst. Dyn. 33(1), 29–48 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin Oehler
    • 1
    Email author
  • Stefan Kohlbrecher
    • 1
  • Oskar von Stryk
    • 1
  1. 1.Technische Universität DarmstadtDarmstadtGermany

Personalised recommendations