Straight-Line Autonomous Walking for Quadrupedal Robots | IEEE Conference Publication | IEEE Xplore

Straight-Line Autonomous Walking for Quadrupedal Robots


Abstract:

The autonomous walking of quadrupedal robots is currently realized mainly through perception, map building, localization, and planning. It also requires expensive hardwar...Show More

Abstract:

The autonomous walking of quadrupedal robots is currently realized mainly through perception, map building, localization, and planning. It also requires expensive hardware such as LIDAR, cameras, Graphics Processing Units(GPU) and so on. The pose of a quadruped robot is mainly estimated from the fusion of an inertial measurement unit(IMU) and kinematics. The integration aspect of the algorithm makes the pose estimation subject to time drift, and the control accuracy of the single-joint motors also affects the accuracy of the pose estimation. In addition, a study shows that the world's top quadrupedal straight-line walking more than 20m or so can be clearly seen lateral offset, its path is not straight, there is a tendency to change to the arc. To address the above issues, we propose a straight-line autonomous walking motion control method based on the position information estimated by IMU and kinematics of a quadruped robotic dog platform, using the coordinate point of the initial position of the autonomous straight-line walking function initiated as the reference system.The quadruped robot developed by Unitree B2 was selected as the experimental platform. The experimental results show that by using a two-dimension plane of joint control and the start point reference system, the method proposed in this paper realizes the autonomous straight-line walking, which enables the quadrupedal robotic dog to ensure that the position is on a straight-line in a Cartesian coordinates coordinate system, and that the heading of the quadruped is in the same direction as that of the straight-line. This improves the system straight-line autonomous walking accuracy, reduces the hardware cost, and reduces the algorithm cost.
Date of Conference: 01-03 November 2024
Date Added to IEEE Xplore: 13 February 2025
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Conference Location: Qingdao, China
References is not available for this document.

I. Introduction

The straight walking function of the robotic dog allows it to move steadily and continuously along a straight path.

Select All
1.
G. Bledt, P. M. Wensing, S. Ingersoll and S. Kim, "Contact Model Fusion for Event-Based Locomotion in Unstructured Terrains", 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4399-4406, 2018.
2.
J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt and S. Kim, "Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control", 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9, 2018.
3.
Donghyun Kim, Benjamin Katz Jared Di Carlo, Gerardo Bledt and Sangbae Kim, "Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control", ArXiv, vol. abs/1909.06586, 2019.
4.
T. Corbères et al., "Comparison of predictive controllers for locomotion and balance recovery of quadruped robots", 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 5021-5027, 2021.
5.
A. Meduri, P. Shah, J. Viereck, M. Khadiv, I. Havoutis and L. Righetti, "BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning", IEEE Transactions on Robotics, vol. 39, no. 2, pp. 905-922, April 2023.
6.
N. J. Kong, C. Li, G. Council and A. M. Johnson, "Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots", IEEE Transactions on Robotics, vol. 39, no. 6, pp. 4712-4727, Dec. 2023.
7.
J Hwangbo, J Lee, A Dosovitskiy et al., "Learning agile and dynamic motor skills for legged robots [J]", Science Robotics, vol. 4, 2019.
8.
O. Melon, M. Geisert, D. Surovik, I. Havoutis and M. Fallon, "Reliable Trajectories for Dynamic Quadrupeds using Analytical Costs and Learned Initializations", 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1410-1416, 2020.
9.
Joonho Lee et al., "Learning quadrupedal locomotion over challenging terrain", Sci. Robot., vol. 5, 2020.
10.
Chuanyu Yang et al., "Multi-expert learning of adaptive legged locomotion", Sci. Robot., vol. 5, 2020.
11.
M. Bjelonic, R. Grandia, M. Geilinger, O. Harley, V. S. Medeiros, V. Pajovic, et al., "Offline motion libraries and online mpc for advanced mobility skills", The International Journal of Robotics Research, pp. 903-924, 2022.
12.
G. Bellegarda and K. Byl, "Trajectory optimization for a wheel-legged system for dynamic maneuvers that allow for wheel slip", 2019 IEEE 58th Conference on Decision and Control (CDC), pp. 7776-7781, 2019.
13.
L. Wellhausen and M. Hutter, "Artplanner: Robust legged robot navigation in the field", Field Robotics, pp. 413-434, 2023.
14.
J. Frey, D. Hoeller, S. Khattak and M. Hutter, "Locomotion policy guided traversability learning using volumetric representations of complex environments", 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5722-5729, 2022.
15.
R. O. Chavez-Garcia, J. Guzzi, L. M. Gambardella and A. Giusti, "Learning ground traversability from simulations", IEEE Robotics and Automation letters, pp. 1695-1702, 2018.
16.
Kevin M. Lynch and Frank C. Park, Modern Robotics: Mechanics Planning and Control, Cambridge:Cambridge University Press, 2017.

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References

References is not available for this document.