Original Research ARTICLE
Combining evolutionary and adaptive control strategies for quadruped robotic locomotion
- 1Institute of BioRobotics, Sant'Anna School of Advanced Studies, Italy
- 2Technical University of Denmark, Denmark
- 3Ghent University, Belgium
In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs.
This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment.
Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space.
Keywords: Evolutionary Algorithm (EA), bio-inspired controller, cerebellum-inspired algorithm, Robotic locomotion, neurorobotics, central pattern generator (CPG)
Received: 15 Apr 2019;
Accepted: 14 Aug 2019.
Edited by:Mario Senden, Maastricht University, Netherlands
Reviewed by:Guoyuan Li, NTNU Ålesund, Norway
Chengju Liu, Yangpu Hospital, Tongji University, China
Copyright: © 2019 Massi, Vannucci, Albanese, Capolei, Vandesompele, Urbain, Sabatini, Dambre, Laschi, Tolu and Falotico. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ms. Elisa Massi, Institute of BioRobotics, Sant'Anna School of Advanced Studies, Pontedera, Italy, email@example.com