Multi-Robot Box Transport over Different Surfaces with Decentralized Role-based Proportional Control

2026-05-25

AI Summary

This work addresses the challenges of coordinating multiple robots to cooperatively push a box across complex terrains with varying friction and slopes, where traditional methods suffer from poor coordination and high failure rates. The authors propose R2P2, an asynchronous decentralized planning framework that dynamically assigns roles—such as pushing, bracing, or anti-slip support—through a role-aware mechanism and integrates rule-based and proportional control strategies for velocity coordination. Notably, R2P2 operates without requiring communication synchronization or global consensus, offering robustness to robot failures and adaptability to changes in terrain and payload. In NVIDIA IsaacSim simulations, R2P2 achieves higher success rates than conventional virtual leader-follower approaches across diverse ground conditions and box masses. Its effectiveness is further validated in physical experiments using four TurtleBots successfully transporting a 1.2 kg box.

Abstract

Collaborative transport of objects via pushing by multiple robots has many applications, ranging from construction and warehouse environments to post disaster debris clean-up. Achieving collaborative transport over surfaces with different inclination and friction properties however poses unique challenges. To address these challenges, this paper presents an asynchronous decentralized task and motion planning approach for transporting rectangular boxes of varying mass over flat, uphill and downhill terrain. Such a decentralized approach alleviates communication, synchronization and consensus needs and mitigates single point of failure issues. Our approach, called R2P2 or Roles with Rules and Proportional-control Primitive, assigns roles (e.g., push, support and prevent) to robots based on rules cognizant of the mode of manipulation needed (box rotation vs translation); this is followed by either rule-based control or proportional control of robot velocity based on the roles. Each robot is assumed to observe the location and heading of self and the box in executing the role and controls. R2P2 is evaluated with a six-robot team deployed in a simulator built using NVIDIA IsaacSim -- demonstrating generalizability across different surface friction/inclination and box mass scenarios, and better success rate compared to a standard virtual-leader-follower method. R2P2 is also successfully validated with a physical experiment, where it is executed onboard four turtlebots tasked with moving a 1.2 kg box.

Problem

• multi-robot
• collaborative transport
• different surfaces
• box pushing
• decentralized control

Innovation

• decentralized control
• multi-robot collaboration
• role-based assignment
• proportional control
• heterogeneous terrain transport