Advancements in Humanoid Robot Locomotion: A Review of Control Strategies and Kinematic Models - Shubham Mishra (International Journal of Robotics and Automation in Mechanics, 2025)
Humanoid robot locomotion has significantly improved over the past few decades, driven by improvements in control strategies and kinematic models. Researchers aim to develop robots that can walk, run, and navigate complex terrains with efficiency and stability. This review explores recent developments in humanoid locomotion, highlighting control strategies such as model predictive control, reinforcement learning, and central pattern generators. Additionally, it examines kinematic models, including inverted pendulum models and zero moment point (ZMP) methods, which play a crucial role in motion planning and stability. The paper discusses challenges, prospects, and potential applications in healthcare, disaster response, and service industries. Furthermore, advancements in sensor technologies and real-time computational capabilities have enhanced the adaptability of humanoid robots, allowing them to function in dynamic environments with greater precision. The integration of artificial intelligence (AI) and biomechanics has also contributed to the refinement of locomotion techniques, leading to improved efficiency and energy optimization. Additionally, researchers are exploring hybrid control strategies that combine physics-based modeling with deep learning techniques, enabling robots to learn and adapt in real-time. The increasing use of cloud-based computing and edge AI is further enhancing the processing speed and decision-making capabilities of humanoid robots, making them more autonomous and responsive. In the coming years, advancements in material science and actuator technologies are expected to improve the durability and flexibility of humanoid robots, allowing for more natural and efficient movement. These innovations will facilitate the deployment of humanoid robots in real-world scenarios, contributing to their use in fields such as elderly care, rehabilitation, search-and-rescue, and hazardous environment exploration.
Keywords: Humanoid robot locomotion; bipedal walking control; energy-efficient robotic motion; kinematic modeling; reinforcement learning in robotics; zero moment point (ZMP) stability