Robust control for multi-legged elongate robots in noisy environments

2025-06-18

AI Summary

Multi-legged elongated robots (MERs) suffer from poor robustness and limited generalizability in highly noisy, unstructured terrains due to challenges in motion control. Method: This paper proposes a novel paradigm inspired by communication theory, synergizing mechanical intelligence (MI) and computational intelligence (CI). It models leg–terrain contact as a fundamental active contact (BAC), analogous to a bit in digital communications; incorporates passive mechanical redundancy—inspired by forward error correction (FEC)—and feedback-based regulation—inspired by automatic repeat request (ARQ)—to unify open-loop robustness with closed-loop adaptability. Contribution/Results: Experiments demonstrate stable forward locomotion at 0.5 body lengths per stride under terrain noise twice that tolerable by conventional robots. The approach significantly enhances cross-platform generalizability and resilience in extreme environments, establishing a principled framework for robust legged locomotion in unstructured settings.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Agent Communication
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Modern two and four legged robots exhibit impressive mobility on complex terrain, largely attributed to advancement in learning algorithms. However, these systems often rely on high-bandwidth sensing and onboard computation to perceive/respond to terrain uncertainties. Further, current locomotion strategies typically require extensive robot-specific training, limiting their generalizability across platforms. Building on our prior research connecting robot-environment interaction and communication theory, we develop a new paradigm to construct robust and simply controlled multi-legged elongate robots (MERs) capable of operating effectively in cluttered, unstructured environments. In this framework, each leg-ground contact is thought of as a basic active contact (bac), akin to bits in signal transmission. Reliable locomotion can be achieved in open-loop on"noisy"landscapes via sufficient redundancy in bacs. In such situations, robustness is achieved through passive mechanical responses. We term such processes as those displaying mechanical intelligence (MI) and analogize these processes to forward error correction (FEC) in signal transmission. To augment MI, we develop feedback control schemes, which we refer to as computational intelligence (CI) and such processes analogize automatic repeat request (ARQ) in signal transmission. Integration of these analogies between locomotion and communication theory allow analysis, design, and prediction of embodied intelligence control schemes (integrating MI and CI) in MERs, showing effective and reliable performance (approximately half body lengths per cycle) on complex landscapes with terrain"noise"over twice the robot's height. Our work provides a foundation for systematic development of MER control, paving the way for terrain-agnostic, agile, and resilient robotic systems capable of operating in extreme environments.

Problem

• Develop robust control for multi-legged robots in noisy environments
• Reduce reliance on high-bandwidth sensing and extensive training
• Integrate mechanical and computational intelligence for reliable locomotion

Innovation

• Uses mechanical intelligence for passive robustness
• Integrates computational intelligence for feedback control
• Applies communication theory to locomotion design