Geometry-aware control enables smooth walk to run obstacle crossing
Geometry-Aware Predictive Control for Walk-to-Run Bipedal Obstacle Crossing
Robotics
Summary
Robots that walk and run can struggle to step over obstacles smoothly without stumbling. The authors present a method that plans the robot’s foot and body movements in understandable steps to safely clear obstacles while transitioning from walking to running. Their system adjusts foot height for obstacle size and body positioning side to side for better balance and clearance. They tested their method in simulations with various obstacles and found it works well up to a challenging limit.
What this means in practice
- •For robotics engineers: Design control software that coordinates bipedal robots' walking and running to navigate obstacles smoothly and safely.
- •For robotic prosthetics developers: Create advanced prosthetic leg controllers that adapt gait phases and obstacle clearance dynamically during transitions between walking and running.
Authors
Aashish Sahu, R. Prasanth Kumar
Abstract
This paper presents a geometry-aware predictive control framework for bipedal obstacle crossing across walking and running regimes. The proposed approach integrates a phase-structured trajectory generator with a constrained model predictive control (MPC) tracking layer to generate coordinated pelvis and swing-foot motions during obstacle traversal. The crossing process is represented through five interpretable phases: pre-crossing, leading-leg clearance, straddle, trailing-leg clearance, and recovery. Within this structure, swing-foot elevation is adapted as a function of obstacle height, while lateral stance widening and pelvis repositioning are introduced as functions of obstacle depth to improve transverse clearance and body alignment. A continuous gait-parameter evolution enables smooth walk-to-run transition without discrete controller switching. The generated references are tracked using MPC to regulate motion smoothness, clearance feasibility, touchdown behavior, and lateral admissibility. The framework is evaluated in reduced-order simulation over five progressively difficult obstacles under walking and running conditions. The robot successfully crosses the first four obstacles, achieving minimum body-obstacle clearances of 0.036, 0.036, 0.059, and 0.017 m, respectively. The fifth and most demanding obstacle leads to collision, identifying the current operating boundary of the proposed reduced-order formulation. Overall, the method provides a compact, interpretable, and computationally efficient basis for intelligent obstacle-aware bipedal locomotion.