Research on Modeling and Energy Consumption Optimization of Humanoid Robot Performance Motions Based on Multi-Stage Trajectory Planning Algorithm

Abstract

With the rapid development of humanoid robot and intelligent service robot technologies, balancing motion stability and energy consumption in complex dynamic performance tasks has become a critical research focus in robot motion planning and control. Aiming at the motion planning and energy management challenges of the Unitree G1 humanoid robot during scientific and technological exhibition performances, this paper establishes a multi-joint motion planning and energy consumption optimization model based on rigid body kinematics and robot dynamics (Spong et al., 2005; Siciliano et al., 2009). The model takes motion feasibility and minimum energy consumption as core goals, integrating key indicators including joint angle-time trajectory, center of mass stability margin, motor power, and energy consumption, and is solved using the C¹ smooth S-type interpolation algorithm (Nguyen et al., 2008; Erkorkmaz and Altintas, 2001), sine trajectory generation algorithm (Siciliano et al., 2009), and numerical simulation optimization algorithm.

Based on the time-joint angle trajectories, a motor power and energy integration model is constructed. Taking the joint motion amplitude scaling factor and motion time scaling factor as decision variables, the multi-parameter search and comparison algorithm based on numerical simulation realizes the energy consumption optimization of the entire performance motion scheme. Quantitative results show that the optimization achieves a 19.2% reduction in total energy consumption compared with the original scheme—with stage-specific energy savings of 11.7% for arm-lifting, 0.3% for straight walking, and 11.7% for the dance climax—while ensuring no significant degradation in motion stability, trajectory accuracy, or visual performance effect. The peak power of the robot is also reduced by 23.5%, effectively alleviating motor load pressure and extending battery endurance under the rated 15Ah/67.2V configuration.

Finally, comprehensive evaluation verifies that the model closely matches the actual structure and working conditions of the Unitree G1, efficiently solving motion planning and energy consumption evaluation problems in the target scenario. It features strong practicality, simple algorithm implementation, and high simulation efficiency, and holds promising application value in humanoid robot stage performance design, complex task gait planning, and energy-saving control of robot motions.