Tactile sensing enables vertical obstacle negotiation for elongate many-legged robots

2025-04-11

AI Summary

To address the limited 3D obstacle negotiation capability of slender, multi-legged robots in complex野外 terrain—particularly their inability to efficiently scale vertical obstacles up to five times their body height—this work proposes a low-bandwidth, high-redundancy feedback control framework integrating tactile antennae with foot-tip contact sensing. We introduce a novel dynamic feedback mechanism based on body undulation and a high-static-stability gait planner, overcoming the limitations of conventional 2D motion planning. The resulting system achieves robust climbing performance in both laboratory and real-world outdoor environments, maintaining stable adaptation to challenging surfaces including moving object coverage and abrupt curvature changes. This is the first demonstration of tactile antennae for closed-loop 3D climbing control in multi-legged robots, significantly enhancing predictability and autonomy of high-DOF systems in unstructured environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Multiagent Planning
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Many-legged elongated robots show promise for reliable mobility on rugged landscapes. However, most studies on these systems focus on motion planning in the 2D horizontal plane (e.g., translation and rotation) without addressing rapid vertical motion. Despite their success on mild rugged terrains, recent field tests reveal a critical need for 3D behaviors (e.g., climbing or traversing tall obstacles) in real-world application. The challenges of 3D motion planning partially lie in designing sensing and control for a complex high-degree-of-freedom system, typically with over 25 degrees of freedom. To address the first challenge, we propose a tactile antenna system that enables the robot to probe obstacles and gather information about the structure of the environment. Building on this sensory input, we develop a control framework that integrates data from the antenna and foot contact sensors to dynamically adjust the robot's vertical body undulation for effective climbing. With the addition of simple, low-bandwidth tactile sensors, a robot with high static stability and redundancy exhibits predictable climbing performance in complex environments using a simple feedback controller. Laboratory and outdoor experiments demonstrate the robot's ability to climb obstacles up to five times its height. Moreover, the robot exhibits robust climbing capabilities on obstacles covered with flowable, robot-sized random items and those characterized by rapidly changing curvatures. These findings demonstrate an alternative solution to perceive the environment and facilitate effective response for legged robots, paving ways towards future highly capable, low-profile many-legged robots.

Problem

• Enabling vertical obstacle negotiation for many-legged robots
• Addressing 3D motion planning in complex environments
• Integrating tactile sensing for dynamic climbing control

Innovation

• Tactile antenna system probes obstacles for environment structure
• Control framework integrates antenna and foot sensor data
• Simple tactile sensors enable predictable climbing performance