Optimization-Based Quadrupedal Hybrid Wheeled-Legged Locomotion

Belli, Italo; Polverini, Matteo Parigi; Laurenzi, Arturo; Hoffman, Enrico Mingo; Rocco, Paolo; Tsagarakis, Nikos G.

doi:10.1109/humanoids47582.2021.9555780

Cited by 3 publications

(2 citation statements)

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“…On the other side, its modularity allows to choose from a vast range of algorithms and mix them together to better suit a desired goal. Several robotics applications demonstrated the capabilities of CasADi as an effective tool to plan dynamic and contact-rich motions, such as in Mercy et al (2016) , Belli et al (2021) , Hoffman et al (2021) , Nguyen and Nguyen (2021) . The aim of this project is creating a robotics-oriented open-source software environment leveraging CasADi strengths that unify the tools to develop these applications in an intuitive way.…”

Section: Related Workmentioning

confidence: 99%

Horizon: A Trajectory Optimization Framework for Robotic Systems

et al. 2022

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This paper presents Horizon, an open-source framework for trajectory optimization tailored to robotic systems that implements a set of tools to simplify the process of dynamic motion generation. Its user-friendly Python-based API allows designing the most complex robot motions using a simple and intuitive syntax. At the same time, the modular structure of Horizon allows for easy customization on many levels, providing several recipes to handle fixed and floating-base systems, contact switching, variable time nodes, multiple transcriptions, integrators and solvers to guarantee flexibility towards diverse tasks. The proposed framework relies on direct simultaneous methods to transcribe the optimal problem into a nonlinear programming problem that can be solved by state-of-the-art solvers. In particular, it provides several off-the-shelf solvers, as well as two custom-implemented solvers, i.e. GN-SQP and Iterative Linear-Quadratic Regulator. Solutions of optimized problems can be stored for warm-starting, and re-sampled at a different frequency while enforcing dynamic feasibility. The proposed framework is validated through a number of use-case scenarios involving several robotic platforms. Finally, an in-depth analysis of a specific case study is carried out, where a highly dynamic motion (i.e., a twisting jump using the quadruped robot Spot® from BostonDynamics1) is generated, in order to highlight the main features of the framework and demonstrate its capabilities.

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Section: Related Workmentioning

confidence: 99%

Horizon: A Trajectory Optimization Framework for Robotic Systems

et al. 2022

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“…To achieve such a level of autonomy, the use of advanced control and planning tools has been fundamental, in particular those allowing for the full exploitation of the dynamics of the system within a prediction horizon. In this respect, Trajectory Optimization (TO) and Model Predictive Control (MPC), among other methods, have proved to be highly effective on legged robotics platforms for a multitude of different tasks, including locomotion and manipulation Koenemann et al (2015) ; Neunert et al (2018) ; Di Carlo et al (2018) ; Kuindersma et al (2016) ; Belli et al (2021) . While TO is commonly used to plan complex open-loop trajectories over a long prediction horizon, MPC enables fast re-planning and feedback stabilization, over a shorter horizon.…”

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confidence: 99%

Editorial: Advancements in trajectory optimization and model predictive control for legged systems

Hoffman¹,

Zhou

Polverini³

2022

Front. Robot. AI

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Editorial on the Research Topic Advancements in trajectory optimization and model predictive control for legged systemsIn recent years we witnessed the proliferation of legged systems as advanced robotic platforms, which could be potentially employed in the near future on a huge spectrum of useful tasks. This is not only related to the success of quadrupeds, e.g. for inspection tasks (Bouman et al. (2020);Gehring et al. (2021)), but also to the increased capabilities of humanoid bipedal systems enabling operation in real-world settings (Agility Robotics, 2021; BostonDynamics (2021a)). To achieve such a level of autonomy, the use of advanced control and planning tools has been fundamental, in particular those allowing for the full exploitation of the dynamics of the system within a prediction horizon. In this respect, Trajectory Optimization (TO) and Model Predictive Control (MPC), among other methods, have proved to be highly effective on legged robotics platforms for a multitude of different tasks, including locomotion and manipulation Koenemann et al. ( 2015); Neunert et al. (2018); Di Carlo et al. (2018); Kuindersma et al. (2016); Belli et al. ( 2021). While TO is commonly used to plan complex open-loop trajectories over a long prediction horizon, MPC enables fast re-planning and feedback stabilization, over a shorter horizon. Thanks to the combination of these techniques, legged systems have been able to successfully demonstrate complex high-level skills like locomanipulation in complex environments, agile locomotion, parkour, and much more in the years to come. However, it still remains an open question how to effectively employ TO and MPC on real legged systems. This requires smart formulations of contactconstrained robot dynamics, convenient models of the environment, as well as computationally efficient and real-time optimization algorithms.

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Voice Controlled Robot Dog

Ioana

Vaduva

2022

2022 IEEE 28th International Symposium for Design and Technology in Electronic Packaging (SIITME)

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Optimization-Based Quadrupedal Hybrid Wheeled-Legged Locomotion

Cited by 3 publications

References 24 publications

Horizon: A Trajectory Optimization Framework for Robotic Systems

Horizon: A Trajectory Optimization Framework for Robotic Systems

Editorial: Advancements in trajectory optimization and model predictive control for legged systems

Voice Controlled Robot Dog

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