Capturability-Aware Footstep Planning in Cluttered Environments: A Recursively Feasible Sequential MPC on Axis-Aligned Safe Corridors
Capturability-Aware Footstep Planning in Cluttered Environments: A Recursively Feasible Sequential MPC on Axis-Aligned Safe Corridors
This research presents a capturability-aware sequential Model Predictive Control (MPC) framework for Linear Inverted Pendulum (LIP)-based humanoid footstep planning in cluttered environments. The method integrates obstacle avoidance with capturability constraints using safe corridors formed by intersecting rectangles, ensuring recursive feasibility for the LIP-based planner. The system autonomously adapts to constrained scenarios, such as switching to in-place walking when forward motion is blocked by unexpected obstacles like closed doors. Performance remains robust across varying prediction horizons, maintaining high success rates with low computational cost. Validation was conducted in high-fidelity simulations using the humanoid robot Digit, demonstrating a practical approach for capturable footstep planning in complex environments.
The paper proposes a capturability-aware sequential MPC framework for humanoid footstep planning that guarantees recursive feasibility and adapts to dynamic obstacles, validated in high-fidelity simulations with the Digit robot; claims of practical deployment readiness are not supported by physical hardware trials.
Source: arXiv Robotics — research abstracts · Read original article ↗
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forward motion. Performance is largely independent of the prediction horizon, allowing high success rates while keeping the computational cost low. The approach is validated in high-fidelity simulations with the humanoid robot Digit, demonstrating a practical solution for capturable humanoid footstep planning in cluttered environments.
Open source S4
bility constraints through safe corridors constructed as sequences of intersecting rectangles, yielding a sequential MPC framework with recursive feasibility guarantees for the LIP-based planning layer. The planner autonomously adapts to constrained scenarios, switching to in-place walking when obstructions---such as doors assumed to be open during planning but unexpectedly found to be closed during execution---block
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org