Dynamic-Based Path Planning and Locomotion of Tensegrity Robots Considering Environmental Interaction

Area of Science:

• Robotics
• Mechanical Engineering
• Control Systems

Background:

• Tensegrity robots offer unique compliance and adaptability but face challenges in dynamic locomotion and environmental interaction.
• Existing path-planning methods often neglect the complex interplay between robot dynamics and environmental contact forces.
• Developing robust locomotion strategies for tensegrity robots requires integrating dynamic modeling with real-time planning.

Purpose of the Study:

• To present a unified, dynamics-based path-planning framework for tensegrity robots that explicitly considers environmental interaction.
• To systematically investigate the relationships between locomotion gaits, actuations, and ground interaction forces in a six-bar tensegrity robot.
• To develop an interaction-aware gait generation and local path-planning strategy for enhanced robotic locomotion.

Main Methods:

• A general dynamic model was established to capture contact friction and generate interaction-aware gaits for a six-bar tensegrity robot.
• A finite gait library was constructed, mapping sequences of gait primitives to desired motions.
• A lightweight local planning strategy (M1L2T3) was formulated on a gait-primitive graph for efficient path selection.

Main Results:

• Experimental validation confirmed the effectiveness of the interaction-coupled dynamic model for gait generation and locomotion control.
• The proposed planning strategy demonstrated successful path selection under varying contact and friction conditions.
• Theoretical predictions showed reasonable agreement with experimental outcomes, validating the framework's efficacy.

Conclusions:

• Incorporating interaction-aware gait realization into locomotion planning is crucial for tensegrity robots.
• The developed framework provides a robust solution for dynamic path planning in complex, interactive environments.
• The study validates the potential of tensegrity robots for advanced locomotion tasks through integrated dynamic modeling and planning.