A simple yet effective whole-body locomotion framework for quadruped robots - Archive ouverte HAL
Article Dans Une Revue Frontiers in Robotics and AI Année : 2020

A simple yet effective whole-body locomotion framework for quadruped robots

Gennaro Raiola
Enrico Mingo Hoffman
  • Fonction : Auteur
Michele Focchi
  • Fonction : Auteur
Nikos G Tsagarakis
  • Fonction : Auteur
Claudio Semini
  • Fonction : Auteur

Résumé

In the context of legged robotics, many criteria based on the control of the Center of Mass (CoM) have been developed to ensure stable and safe robot locomotion. Defining a whole-body framework with the control of the CoM requires a planning strategy, often based on a specific type of gait and reliable state-estimation. In a whole-body control approach, if the CoM task is not specified, the consequent redundancy can still be resolved by specifying a postural task that sets references for all the joints. Therefore, the postural task can be exploited to keep a well behaved, stable kinematic configuration. In this work, we propose a generic locomotion framework which is able to generate different kind of gaits, ranging from very dynamic gaits such as the trot, to more static gaits like the crawl, without the need to plan the CoM trajectory. Consequently, the whole-body controller becomes planner-free and it does not require the estimation of the floating base state, which is often prone to drift. The framework is composed of a priority-based whole-body controller that works in synergy with a walking pattern generator. We show the effectiveness of the framework by presenting simulations on different types of simulated terrains, including rough terrain, using different quadruped platforms.
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Dates et versions

hal-03005133 , version 1 (13-11-2020)
hal-03005133 , version 2 (14-03-2023)

Identifiants

  • HAL Id : hal-03005133 , version 2

Citer

Gennaro Raiola, Enrico Mingo Hoffman, Michele Focchi, Nikos G Tsagarakis, Claudio Semini. A simple yet effective whole-body locomotion framework for quadruped robots. Frontiers in Robotics and AI, 2020. ⟨hal-03005133v2⟩
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