Adaptive Gait Control for Quadruped Robots on Varied Slopes via ARS Algorithm
Received: 26 November 2024 | Revised: 29 December 2024 and 12 January 2025 | Accepted: 18 January 2025 | Online: 26 February 2025
Corresponding author: Van-Truong Nguyen
Abstract
Ensuring stable walking for quadruped robots on unknown slopes is a critical challenge in robotic navigation. This study introduces a novel gait planning algorithm that leverages data from an Inertial Measurement Unit (IMU) for terrain slope estimation, offering a cost-effective alternative to visual sensors. The proposed approach integrates a trot gait with an elliptical foot trajectory, enabling efficient movement across varied slopes. Using the Augmented Random Search (ARS) algorithm, we fine-tune the elliptical trajectory parameters to achieve precise and adaptive foot placements. Additionally, the robot dynamically adjusts its posture in real time to maintain stability by aligning with desired joint angles during slope traversal. Simulation results validate the effectiveness of the proposed algorithm, demonstrating its ability to ensure stable and adaptive locomotion on slopes of up to 11 degrees. This work highlights the feasibility of using low-cost hardware and advanced algorithms to address complex terrain navigation challenges.
Keywords:
reinforcement learning, Augmented Random Search (ARS), artificial intelligence, quadruped robot, robot stabilityDownloads
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Copyright (c) 2025 Van-Truong Nguyen, Ngoc-Quyet Nguyen, Thanh-Lam Bui
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