Analýza pokročilých řídicích algoritmů pro chůzi čtyřnohých robotů se zaměřením na užití v simulaci
Analysis of State-of-the-Art Quadruped Locomotion Controllers for Use in Simulation
Typ dokumentu
bakalářská prácebachelor thesis
Autor
Jakub Jon
Vedoucí práce
Pecka Martin
Oponent práce
Szadkowski Rudolf Jakub
Studijní program
Kybernetika a robotikaInstituce přidělující hodnost
katedra kybernetikyPráva
A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.htmlVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html
Metadata
Zobrazit celý záznamAbstrakt
The field of robotics has experienced a surge of advanced robots entering the market in recent years. Many robots today can perform tasks that were unimaginable only a few years ago. Among the various types of robots—such as drones, bipedal robots, or hexapods —four-legged robots have gained significant attention due to their ability to navigate complex terrains and perform dynamic movements.. In this work, we explore state-of-the-art methods for controlling quadrupedal robots. Specifically, we focus on implementing four walking controllers for the Anymal D robot, one of the most advanced four-legged machines available. These controllers include a model predictive controller (MPC) in combination with a whole-body tracking controller, a reinforcement-learning-based controller, and two hybrid controllers that combine an MPC controller with a neural-network based tracking controller. We verify the functionality and performance of these controllers in the Gazebo simulator, evaluating their ability to effectively traverse various types of terrains. Finally, we develop a context-aware strategy that dynamically switches between two of the implemented controllers based on the available information. The field of robotics has experienced a surge of advanced robots entering the market in recent years. Many robots today can perform tasks that were unimaginable only a few years ago. Among the various types of robots—such as drones, bipedal robots, or hexapods —four-legged robots have gained significant attention due to their ability to navigate complex terrains and perform dynamic movements. In this work, we explore state-of-the-art methods for controlling quadrupedal robots. Specifically, we focus on implementing four walking controllers for the Anymal D robot, one of the most advanced four-legged machines available. These controllers include a model predictive controller (MPC) in combination with a whole-body tracking controller, a reinforcement-learning-based controller, and two hybrid controllers that combine an MPC controller with a neural-network based tracking controller. We verify the functionality and performance of these controllers in the Gazebo simulator, evaluating their ability to effectively traverse various types of terrains. Finally, we develop a context-aware strategy that dynamically switches between two of the implemented controllers based on the available information.
Kolekce
- Bakalářské práce - 13133 [851]