Legged robots, particularly torso-less bipedal robots, face significant challenges in navigating real-world terrains such as slopes, stairs, and uneven surfaces due to inadequate footstep planning and poor adaptability. Existing designs often lack mechanical features and control strategies to maintain dynamic stability, resulting in inefficient walking patterns and limited terrain coverage. Moreover, insufficient consideration of the centre of gravity (COG) shifting and support polygon management restricts stable movement on inclined or irregular surfaces. To address the challenges of stable locomotion on uneven terrains, this research introduces a torso-less bipedal robotic framework for stable locomotion on uneven terrains. The mechanical model is optimized with widened feet and calibrated torso offsets to maintain a stable support polygon. Building on this foundation, the path trajectory planning module generates optimized trajectories based on slope, step height, and terrain conditions. These trajectories are translated into precise joint movements through an Inverse Kinematics (IK) algorithm, ensuring accurate foot placement on uneven terrain. To further enhance stability and dynamic response, a Model Predictive Control (MPC) system, using an inverted pendulum model to continuously adjusts the centre of gravity (COG) and overall body posture based on real-time sensor feedback. The integrated system reduces step time, improves walking speed, and enhances energy efficiency. As a result, the proposed robot shows superior adaptability and performance of enabling robust, stable locomotion across unpredictable terrains than the existing designs.
Krishnaswmy et al. (Sat,) studied this question.