Nuwan Jaliyagoda

The project is Ongoing and the further details will be updated in here soon.

Abstract:

Problem: Shape-changing robots offer significant potential for navigating dynamic environments, yet existing motion planning algorithms, primarily designed for rigid-body robots, fail to meet the demands of dynamic morphology. Effective motion planning must address real-time structural adaptation, computational efficiency, and resource constraints while ensuring optimal movement strategies.

Methodology: This study introduces a Reinforcement Learning framework to enhance motion planning in shape-changing robots. By leveraging internal sensor data, including acceleration and velocity in multiple axes, the system continuously optimizes morphology and movement strategies. A mixed-methods approach evaluates model performance in both simulated and physical environments.

Results: The RL-based framework improved adaptability and efficiency in shape-changing robots, enabling real-time morphology adjustments for optimized movement. Simulated and preliminary real-world tests confirmed enhanced locomotion stability over traditional methods. The model demonstrated effective learning with minimal computational overhead, supporting further refinements for real-time applications.