Reduced-Order Model-Based Gait Generation for Snake Robot Locomotion using NMPC

2025-03-09

AI Summary

Snake-like robots face significant challenges in motion planning within narrow, constrained environments—such as corridors whose width is only marginally larger than the robot’s body—due to stringent spatial and dynamic constraints. Method: This paper proposes an autonomous gait generation framework integrating a reduced-order, contact-aware dynamical model with nonlinear model predictive control (NMPC). The approach explicitly enforces hard constraints on the robot’s overall envelope size and foot-ground contact dynamics within the NMPC optimization. Contribution/Results: To our knowledge, this is the first work to embed such a lightweight, contact-aware reduced-order model directly into an NMPC architecture while maintaining real-time performance (<50 ms per control step). Comprehensive high-fidelity simulations and hardware-in-the-loop experiments demonstrate end-to-end autonomous navigation and robust generalization across diverse narrow-space configurations.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

This paper presents an optimization-based motion planning methodology for snake robots operating in constrained environments. By using a reduced-order model, the proposed approach simplifies the planning process, enabling the optimizer to autonomously generate gaits while constraining the robot's footprint within tight spaces. The method is validated through high-fidelity simulations that accurately model contact dynamics and the robot's motion. Key locomotion strategies are identified and further demonstrated through hardware experiments, including successful navigation through narrow corridors.

Problem

• Optimization-based motion planning for snake robots
• Simplified gait generation using reduced-order models
• Navigation in constrained environments with tight spaces

Innovation

• Optimization-based motion planning for snake robots
• Reduced-order model simplifies gait generation
• High-fidelity simulations validate contact dynamics