[Paper Review] Loosely Coupled Payload Transport System with Robot Replacement

Pulkit Verma, Rahul Tallamraju+3 more|arXiv (Cornell University)|Apr 5, 2019

Robotic Path Planning Algorithms23 references4 citations

TL;DR

This paper proposes a loosely coupled multi-robot payload transport system with automated robot replacement to extend operational time beyond individual robot battery limits. Using a centralized optimization framework, it schedules low-battery robots to be replaced by charged robots at peripheral recharge hubs, achieving up to 60 minutes of operation with payload—20 minutes beyond the baseline 40-minute non-payload limit—via real-world experiments and simulations with five-robot formations.

ABSTRACT

In this work, we present an algorithm for robot replacement to increase the operational time of a multi-robot payload transport system. Our system comprises a group of nonholonomic wheeled mobile robots traversing on a known trajectory. We design a multi-robot system with loosely coupled robots that ensures the system lasts much longer than the battery life of an individual robot. A system level optimization is presented, to decide on the operational state (charging or discharging) of each robot in the system. The charging state implies that the robot is not in a formation and is kept on charge whereas the discharging state implies that the robot is a part of the formation. Robot battery recharge hubs are present along the trajectory. Robots in the formation can be replaced at these hub locations with charged robots using a replacement mechanism. We showcase the efficacy of the proposed scheduling framework through simulations and experiments with real robots.

Motivation & Objective

• To address the critical limitation of single-robot battery life in coordinated multi-robot payload transport tasks.
• To design a system that maintains continuous payload transport beyond the battery life of any individual robot.
• To develop a scheduling algorithm that minimizes unnecessary robot replacements while ensuring system continuity.
• To enable autonomous, human-free robot replacement using support robots at designated recharge hubs.
• To validate the system’s effectiveness through real hardware experiments and Gazebo simulations.

Proposed method

• A multi-robot system uses a leader-follower formation with decentralized control based on relative pose (distance and angle) to maintain payload stability.
• Battery levels are monitored via on-board current and voltage sensors and shared with a central server for optimization.
• An integer quadratic program (IQP) is formulated to determine which robots should be in formation (discharging) and which should be at recharge hubs (charging), using CPLEX for solution.
• The optimization minimizes replacements while ensuring system operation, with binary decision variables indicating robot state (1 = in formation, 0 = at hub).
• Physical robot replacement is executed using support robots at hubs, which assist in lifting and repositioning robots to maintain payload balance during swaps.
• A support robot is used only when necessary to prevent payload instability, particularly during multiple or complex replacements.

Experimental results

Research questions

• RQ1 How can a multi-robot payload transport system maintain continuous operation beyond the battery life of individual robots?
• RQ2 What optimization strategy minimizes the number of robot replacements while ensuring system stability and task continuity?
• RQ3 How can autonomous, human-free robot replacement be achieved in a moving formation with minimal payload disturbance?
• RQ4 What is the achievable operational duration of a payload transport system using robot replacement with real hardware and simulation?
• RQ5 How does the system perform under multiple replacements at a single hub or across multiple hubs?

Key findings

• The system achieved 40 minutes of operational time without payload and 25 minutes with a 6 kg payload using only the initial robot battery capacity.
• With the proposed robot replacement algorithm, the system extended operational time to approximately 60 minutes when carrying the payload, a 20-minute improvement.
• Each robot replacement process took approximately 3 minutes in real-world experiments, with stable payload handling maintained through support robots.
• The system successfully handled multiple replacements at a single hub and across multiple hubs, demonstrating scalability and robustness.
• The Gazebo simulation validated the system’s behavior with nine robots, five in formation and three recharge hubs, each hosting one charged robot.
• The optimization framework successfully identified low-battery robots and available charged robots at hubs, enabling autonomous scheduling without human intervention.