Multi-Robot Cooperative Tracking System (LIMO Robots)

This repository contains the implementation of a multi-robot cooperative tracking system developed for the Intelligent Distributed Systems course.

The goal is to enable a team of mobile robots (LIMO) to collaboratively localize and track a moving human agent in a shared environment while maintaining a coordinated formation around the target. The system combines decentralized estimation, vision-based perception, and model-based control.

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System Overview

The system consists of three LIMO mobile robots that cooperatively track a moving person while maintaining a coordinated formation around the target. The robots combine vision-based perception, cooperative state estimation, and model-based control to ensure consistent relative positioning with respect to the human agent.

Each robot operates with local sensing and contributes to a shared estimation process through inter-agent measurements.

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Core Methods

Cooperative State Estimation

• Implemented an Interacting Multiple Model / decentralized EKF-based cooperative localization framework
• Enables inter-robot relative measurements
• Fusion of:
  • wheel odometry
  • stereo camera measurements
  • relative observations between agents

This allows consistent multi-agent localization even under partial observability.

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Perception (Vision)

• Marker-based detection for inter-robot and robot-person observations
• Multi-source visual fusion for robust target identification

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Control

• Model Predictive Control (MPC) for trajectory tracking and formation maintenance
• Each robot computes local control actions to:
  • track the moving target
  • preserve formation constraints relative to other agents
  • ensure smooth coordination in dynamic environments

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System Architecture

Each LIMO robot runs a local pipeline:

1. Perception:

   • Marker detection

2. State Estimation:

   • Cooperative EKF with inter-agent measurements
   • Fusion of odometry + stereo vision + relative observations

3. Control:

   • MPC-based motion controller
   • local trajectory generation for tracking and formation keeping

Communication between agents is handled using ROS2 topics.

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Technologies

• Python
• ROS2
• OpenCV
• NumPy
• Nonlinear estimation (EKF)
• Model Predictive Control (MPC)
• Stereo vision

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Key Features

• Decentralized multi-robot state estimation
• Cooperative localization using inter-agent measurements
• Marker-based robot-to-robot detection
• MPC-based control in dynamic environments
• Formation-based multi-robot tracking of a moving human
• Simulation-first → real robot deployment pipeline

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Objectives

• Maintain a dynamic formation of robots around a moving human target
• Achieve robust multi-robot tracking of a moving human
• Maintain consistent localization under uncertainty
• Leverage inter-robot cooperation to improve estimation accuracy
• Transition from simulation to real-world robotic deployment

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Author

Marco Misseroni and Federico Battisti
MSc Mechatronics Engineering – Electronics and Robotics
University of Trento