Hierarchical RL-Guided Large-scale Navigation of a Snake Robot (2312.03223v1)

Abstract: Classical snake robot control leverages mimicking snake-like gaits tuned for specific environments. However, to operate adaptively in unstructured environments, gait generation must be dynamically scheduled. In this work, we present a four-layer hierarchical control scheme to enable the snake robot to navigate freely in large-scale environments. The proposed model decomposes navigation into global planning, local planning, gait generation, and gait tracking. Using reinforcement learning (RL) and a central pattern generator (CPG), our method learns to navigate in complex mazes within hours and can be directly deployed to arbitrary new environments in a zero-shot fashion. We use the high-fidelity model of Northeastern's slithering robot COBRA to test the effectiveness of the proposed hierarchical control approach.

• The paper introduces a four-layer hierarchical RL-CPG framework that enables snake robots to dynamically adapt and navigate complex terrains.
• It combines global path planning, ego-centric perception, and rapid RL training to achieve zero-shot deployment in diverse maze environments.
• Simulation results on the COBRA robot demonstrate significant training time reductions and enhanced maneuverability for autonomous exploratory missions.

Introduction to Hierarchical Control in Robotics

In the field of robotics, efficient navigation in complex environments poses a significant challenge, especially for robots with unconventional locomotion methods such as slithering. Traditional control strategies in snake robotics primarily replicate the distinct locomotive patterns of their biological counterparts, using techniques that focus on stable movement across flat landscapes. However, with the rise of machine learning techniques such as Reinforcement Learning (RL), new possibilities have emerged to enhance these robots' adaptability and autonomy in navigating unstructured terrains.

Advancements in Snake Robot Navigation

The paper introduces a four-layer hierarchical control scheme for a snake robot, which utilizes RL along with a central pattern generator (CPG) to dynamically, and adaptively navigate in large-scale environments like complex mazes. Interestingly, the model allows for rapid learning within hours and can seamlessly adapt to novel environments without the need for retraining—a technique referred to as zero-shot deployment.

The underlying control approach incorporates global path planning to map out efficient routes through unknown terrains, ego-centric perception that leverages on-board sensors, RL to handle local navigation challenges, and gait tracking for smooth locomotion. This innovative combination reduces the overall complexity of navigation tasks and significantly accelerates the training process of RL algorithms, making it a promising solution for controlling robots with high degrees of freedom in contact-rich environments.

Methodology and Simulation Results

The strategy is validated on the Northeastern University COBRA robot, which is designed for space exploration and equipped to handle uneven lunar terrains. Unlike earlier methods that relied on simplified models and controlled environments, the presented RL-CPG scheme is model-free, requiring no prior knowledge of the terrain for generating adaptable gaits.

The approach's efficacy is demonstrated through simulation results that showcase the robot's ability to perform key navigational maneuvers, such as straight-line slithering and efficient turning at junctions. Beyond agility in single maneuvers, the control system ensures COBRA can navigate randomly generated mazes with varying layouts and sizes.

Furthermore, the performance of the RL-CPG control scheme is juxtaposed against traditional RL methods that directly apply learning to the robot's joints. This comparison shows a significant advantage of the proposed method, most notably in terms of training time, highlighting the efficiency gains associated with hierarchical and hybrid learning-control approaches.

Conclusion and Implications

The investigation concludes that coupling hierarchical control with RL-CPG models presents a powerful solution to classical control challenges faced by highly articulated snake robots in large-scale navigation. The technique's ability to generalize across new environments without the need for retraining or external motion tracking systems presents a leap forward for autonomous robotic exploration.

While the scope of the research focuses on simulation-based evaluations, future work will likely explore the transferability of the control framework to physical hardware. The implications of such advancements are far-reaching, potentially aiding complex exploratory missions where conventional wheeled or legged robots may falter.