Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion
| العنوان: | Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion |
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| المؤلفون: | Egidio Falotico, Angelo Maria Sabatini, Gabriel Urbain, Cecilia Laschi, Lorenzo Vannucci, Elisa Massi, Alexander Vandesompele, Silvia Tolu, Joni Dambre, Ugo Albanese, Marie Claire Capolei |
| المصدر: | Frontiers in Neurorobotics Massi, E, Vannucci, L, Albanese, U, Capolei, M C, Vandesompele, A, Urbain, G, Maria Sabatini, A, Dambre, J, Laschi, C, Tolu, S & Falotico, E 2019, ' Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion ', Frontiers in Neurorobotics, vol. 13, 71 . https://doi.org/10.3389/fnbot.2019.00071 FRONTIERS IN NEUROROBOTICS Frontiers in Neurorobotics, Vol 13 (2019) |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Technology and Engineering, Adaptive control, Computer science, 0206 medical engineering, Biomedical Engineering, Evolutionary algorithm, Cerebellum-inspired algorithm, 02 engineering and technology, lcsh:RC321-571, Bio-inspired controller, Neurorobotics, neurorobotics, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Robotic locomotion, Central pattern generator, bio-inspired controller, Legged robot, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Original Research, evolutionary algorithm, business.industry, Robotics, Control engineering, Motion control, 020601 biomedical engineering, cerebellum-inspired algorithm, central pattern generator, robotic locomotion, INTERNAL-MODELS, Robot, Artificial intelligence, SPINAL-CORD, business, 030217 neurology & neurosurgery, Neuroscience |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1662-5218 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5031badfa741e966ef78b9f72012e728 http://hdl.handle.net/11382/529996 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5031badfa741e966ef78b9f72012e728 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 16625218 |
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