GRF-based trajectory planning and application for bipedal motion
Year: 2026
Type: article
Abstract: Trajectory planning for the center of mass (CoM) constitutes a fundamental challenge in bipedal locomotion. Conventional approaches, which predominantly rely on experimental measurements or simplified analytical models, are often constrained by discretization errors, parameter uncertainty, and prohibitive experimental costs. To address these limitations, this paper proposes a numerical framework for generating CoM trajectories directly from ground reaction force (GRF) dynamics. The methodology first determines key gait event positions and formulates a coupled system of governing equations that integrate force and motion. The method then establishes a unified spatio-temporal-GRF coordinate system and incorporates boundary conditions, along with load-sharing mechanisms between the trailing and leading legs, to derive the force and geometric constraint equations. The resulting reference trajectory is represented using Fourier coefficients, which are determined via an Adam-based constrained residual minimization solver. This Fourier-based representation offers notable advantages, including a compact parameter set, high computational efficiency, and the capacity to capture the essential kinematic features of bipedal walking. To validate the proposed reference trajectory, a sliding mode control (SMC) framework is employed for bipedal walking control, in which the walker successfully tracks the reference trajectory with stable gait execution. The results confirm that the generated trajectory faithfully reproduces the characteristic CoM response of bipedal walking, thereby providing a high-fidelity reference for robust motion control. Furthermore, a systematic analysis of the control parameters is conducted to evaluate their influence on walking stability. This work offers a new paradigm that synergistically combines physical interpretability with computational efficiency, holding significant promise for applications in humanoid robot motion control and biomechanical analysis.
DOI: 10.59400/adecp4582
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204238490