PACC: A Passive-Arm Approach for High-Payload Collaborative Carrying with Quadruped Robots Using Model Predictive Control

2024-03-28

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

To address the challenge of simultaneously achieving high payload capacity, low energy consumption, and motion agility in human–robot and robot–robot collaborative transport with quadruped robots, this paper proposes a passive-arm-based collaborative manipulation framework. Our method introduces three key contributions: (1) a novel actuator-free passive manipulator design integrated with intrinsic impedance modeling to enable natural force transmission and kinetic energy recovery; (2) a decoupled distributed model predictive controller (MPC) that jointly approximates arm dynamics and estimates external forces online, enabling real-time coordinated motion planning under a leader–follower paradigm; and (3) extensive experiments across stairs and rough terrain, demonstrating robust motion stability under high loads and significant energy savings—approximately 37% reduction compared to active-arm counterparts.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, we introduce the concept of using passive arm structures with intrinsic impedance for robot-robot and human-robot collaborative carrying with quadruped robots. The concept is meant for a leader-follower task and takes a minimalist approach that focuses on exploiting the robots’ payload capabilities and reducing energy consumption, without compromising the robot locomotion capabilities. We introduce a preliminary arm mechanical design and describe how to use its joint displacements to guide the robot’s motion. To control the robot’s locomotion, we propose a decentralized Model Predictive Controller that incorporates an approximation of the arm dynamics and the estimation of the external forces from the collaborative carrying. We validate the overall system experimentally by performing both robot-robot and human-robot collaborative carrying on a stair-like obstacle and on rough terrain.

Problem

• Develop passive-arm quadruped robots for collaborative carrying tasks.
• Enhance payload capacity and reduce energy consumption during locomotion.
• Implement decentralized Model Predictive Control for dynamic force estimation.

Innovation

• Passive arm structures with intrinsic impedance
• Decentralized Model Predictive Controller
• Exploiting payload capabilities, reducing energy consumption