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Concurrent Optimization of Mechanical Design and Locomotion Control of a Legged Robot

  • Authors: Digumarti, K. M.; Gehring, C.; Coros, S.; Hwangbo, J.; Siegwart, R.

This paper introduces a method to simultaneously optimize design and control parameters for legged robots to improve the performance of locomotion based tasks. The morphology of a quadrupedal robot was optimized for a trotting and bounding gait to achieve a certain speed while tuning the control parameters of a robust locomotion controller at the same time. The results of the optimization show that a change of the structure of the robot can help increase its admissable top speed while using the same actuation units.

Posted on: July 16, 2014

On the Comparison of Gauge Freedom Handling in Optimization-Based Visual-Inertial State Estimation

  • Authors: Zhang, Zichao; Gallego, Guillermo; Scaramuzza, Davide

  It is well known that visual-inertial state estimation is possible up to a four degrees-of-freedom (DoF) transformation (rotation around gravity and translation), and the extra DoFs (“gauge freedom”) have to be handled properly. While different approaches for handling the gauge freedom have been used in practice, no previous study has been carried out to …

Posted on: June 12, 2018

Trajectory Optimization for Wheeled-Legged Quadrupedal Robots Driving in Challenging Terrain

Authors: Suzano Medeiros,V.; Jelavic, E.; Bjelonic, M.; Siegwart, R.; Meggiolaro, M. A.; Hutter, M.


Wheeled-legged robots are an attractive solution for versatile locomotion in challenging terrain. They combine the speed and efficiency of wheels with the ability of legs to traverse challenging terrain. In this paper, we present a trajectory optimization formulation for wheeled-legged robots that optimizes over the base and wheels’ positions and forces and takes into account the terrain information while computing the plans. This enables us to find optimal driving motions over challenging terrain. The robot is modeled as a single rigid-body, which allows us to plan complex motions and still keep a low computational complexity to solve the optimization quickly. The terrain map, together with the use of a stability constraint, allows the optimizer to generate feasible motions that cannot be discovered without taking the terrain information into account. The optimization is formulated as a Nonlinear Programming (NLP) problem and the reference motions are tracked by a hierarchical whole-body controller that computes the torque actuation commands for the robot. The trajectories have been experimentally verified on quadrupedal robot ANYmal equipped with non-steerable torque-controlled wheels. Our trajectory optimization framework enables wheeled quadrupedal robots to drive over challenging terrain, e.g., steps, slopes, stairs, while negotiating these obstacles with dynamic motions.


  • Published in: EEE Robotics and Automation Letters
  • Detailed record: yy
  • DOI: 10.1109/LRA.2020.2990720
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  • Date: 2020
Posted on: May 10, 2020