State estimation for legged robots
This report investigates the effectiveness of the current state estimation algorithm for quadruped robots that is suitable in a non-GPS denied environment, focusing on the application of Kalman Filters (Linear and Extended Kalman Filter). The primary objective is to evaluate its performance and comp...
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2024
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sg-ntu-dr.10356-1760162024-05-18T16:53:08Z State estimation for legged robots Yap, Zhen Yan Domenico Campolo School of Mechanical and Aerospace Engineering Institute for Infocomm Research (I2R) Agency for Science, Technology and Research (A*STAR) d.campolo@ntu.edu.sg Engineering State estimation Kalman filter Quadruped robots This report investigates the effectiveness of the current state estimation algorithm for quadruped robots that is suitable in a non-GPS denied environment, focusing on the application of Kalman Filters (Linear and Extended Kalman Filter). The primary objective is to evaluate its performance and compare it with the newly implemented algorithms. In this report, Unitree’s Go1 quadruped robot is employed, utilizing its on-board sensors such as Inertia Measurement Unit, joint encoders and foot force sensors within the Kalman Filter framework. Additionally, supplementary cameras are incorporated by mounting them onto the robot to analyse their impact on the estimated state output. Furthermore, data collection was performed on two scenarios: flat ground and on the stairs, to test the robustness of the methods. To evaluate the effectiveness of each algorithm, the estimated state from various methods is being compared with the actual trajectory which was obtained from the OptiTrack cameras. Through comprehensive evaluation, it was found that the implemented algorithm that had visual aid (LKF-VILO) produced the most favourable outcome obtaining the lowest Root Mean Square Error in all 3 dimensions. These findings underscore the importance of state estimation and the need to further improve and advance in state estimation techniques for legged robotic systems, ensuring a much more efficient localization process. Bachelor's degree 2024-05-13T04:45:16Z 2024-05-13T04:45:16Z 2024 Final Year Project (FYP) Yap, Z. Y. (2024). State estimation for legged robots. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176016 https://hdl.handle.net/10356/176016 en P-A209 application/pdf Nanyang Technological University |
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Engineering State estimation Kalman filter Quadruped robots |
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Engineering State estimation Kalman filter Quadruped robots Yap, Zhen Yan State estimation for legged robots |
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This report investigates the effectiveness of the current state estimation algorithm for quadruped robots that is suitable in a non-GPS denied environment, focusing on the application of Kalman Filters (Linear and Extended Kalman Filter). The primary objective is to evaluate its performance and compare it with the newly implemented algorithms. In this report, Unitree’s Go1 quadruped robot is employed, utilizing its on-board sensors such as Inertia Measurement Unit, joint encoders and foot force sensors within the Kalman Filter framework. Additionally, supplementary cameras are incorporated by mounting them onto the robot to analyse their impact on the estimated state output. Furthermore, data collection was performed on two scenarios: flat ground and on the stairs, to test the robustness of the methods. To evaluate the effectiveness of each algorithm, the estimated state from various methods is being compared with the actual trajectory which was obtained from the OptiTrack cameras. Through comprehensive evaluation, it was found that the implemented algorithm that had visual aid (LKF-VILO) produced the most favourable outcome obtaining the lowest Root Mean Square Error in all 3 dimensions.
These findings underscore the importance of state estimation and the need to further improve and advance in state estimation techniques for legged robotic systems, ensuring a much more efficient localization process. |
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Domenico Campolo |
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Domenico Campolo Yap, Zhen Yan |
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Final Year Project |
author |
Yap, Zhen Yan |
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Yap, Zhen Yan |
title |
State estimation for legged robots |
title_short |
State estimation for legged robots |
title_full |
State estimation for legged robots |
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State estimation for legged robots |
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State estimation for legged robots |
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state estimation for legged robots |
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Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/176016 |
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1806059861446754304 |