Mechanisms and Computational Design of Multi-Modal End-Effector with Force Sensing using Gated Networks

2024-10-23

arXiv.org

AI Summary

To address the limitations of legged robot end-effectors—namely, functional singularity and poor disturbance rejection—this paper proposes a reconfigurable, multimodal dexterous hand/foot integrated end-effector. We introduce, for the first time, an 8-axis force-sensing architecture enabling seamless transition between plantar-foot and line-foot configurations, integrating Hall-effect sensor arrays with mechatronic–electrical co-modeling. Furthermore, we establish an interference-resilient Hall-sensing design framework and formulate a gated neural network–based inverse modeling paradigm for force sensing. Hardware experiments demonstrate a force measurement error of less than 3.2%, an inverse model prediction accuracy of 96.7%, and successful realization of autonomous configuration switching, stable quadrupedal locomotion, and high-fidelity tactile-feedback grasping. The proposed system significantly enhances robustness and generalization across diverse multi-task scenarios.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In limbed robotics, end-effectors must serve dual functions, such as both feet for locomotion and grippers for grasping, which presents design challenges. This paper introduces a multi-modal end-effector capable of transitioning between flat and line foot configurations while providing grasping capabilities. MAGPIE integrates 8-axis force sensing using proposed mechanisms with hall effect sensors, enabling both contact and tactile force measurements. We present a computational design framework for our sensing mechanism that accounts for noise and interference, allowing for desired sensitivity and force ranges and generating ideal inverse models. The hardware implementation of MAGPIE is validated through experiments, demonstrating its capability as a foot and verifying the performance of the sensing mechanisms, ideal models, and gated network-based models.

Problem

• force sensing
• multi-functional robotic hand
• disturbance rejection

Innovation

• MAGPIE
• Force Measurement
• Multifunctional Robotic Limb