Multi-robot Human-aware Navigation

Continual developments in robotic technology have enabled having robots in everyday applications in domestic, office and public places. One common key factor of all these environments is the presence of people and all the technical and social challenges it brings. Gaining acceptance of the people by providing satisfactory levels of safety, comfort, and conforming to basic social rules along with efficiency in performing the given tasks are essential requirements for advancement of robotic applications in our society. However, such applications will not be limited to a single robot as there will be increasing demand for robotic assistants and multi-robot applications. In comparison with their individual counterparts, multi-robot systems, can exhibit superior properties that cannot be achieved without exploiting the potential of the team by means of information sharing, coordination and joint decision-making. Additionally, multi-robot teams are capable of increased efficiency by distributing or parallelizing the required workload. Therefore, human-aware cooperative multi-robot systems will be a necessity if teams of robots are to operate in real applications in environments shared with people.


This research focuses on multi-robot coordination in social human-populated environments using a market-based framework for solving the Multi-Robot Task Allocation (MRTA) problem. The main difficulty for MRTA in such highly dynamic and noisy environments is that plans are likely to change or to be rendered invalid, particularly if the robots are planning for long periods of time. Additionally, the robots are required to perform in a socially acceptable manner in terms of navigation and interaction with people and other team members. The combination of human-aware coordination, human-aware task planning, and individual human-aware navigation is shown to improve the performance of the robot team in terms of MRTA metrics such as the total traveled distance and time, as well as the social awareness of the robots.

This research project is part of the European project of MOnarCH.

Team and Collaborators

In collaboration with:

Research Period and Sponsors

This project started in April 2013 and is still ongoing.

NCCR robotics sponsored this project from April 2013 to March 2014. MOnarCH was the sponsor of this project from March 2014 to March 2016.


DISAL-SU28: Paul Prevel, Application of Human-Robot Interaction (HRI) in Multi-Robot Task Allocation (MRTA) in a social environment

DISAL-SP118: Paul Prevel, Integrating Human-Robot Interaction (HRI) with Cooperative Human-aware Navigation for Social Environment

EIRATECH-DISAL-MP39: Cyrill Baumann, Distributed vs Centralized Path-Planning and Task-Assignment Solutions for a Fleet of Mobile Warehouse Robots

DISAL-SP111:  Nicolas Talabot, Human-Aware Navigation Using Kinect-based Active Perception

DISAL-SP109:  Paul Alderton, Market-based Coordination for Social Robots in Highly Dynamic Environment

DISAL-SP101: Wilson Colin, Human-aware Navigation in Populated Environment with Special Focus on Group Interactions

DISAL-SP98: Alaa Bakr Maghrabi, Ultra-Wideband Localization in for Person Tracking

DISAL-SP91: Stefano Savare, Market-based coordination for social robot in human-populated environment

DISAL-IP31: Jose Manuel Palacios Gasos, Optimal Path Planning and Coverage Control for Multi-Robot Persistent Coverage in Environments with Obstacles

DISAL-SP84: Christophe Reiners, Ultra-Wideband Localization in Multi-Robot Systems for Person Tracking

DISAL-SP83: Audrey Marullaz, Predictive Person Following using MOnarCH Robots

DISAL-MP26: Alessio Canepa, Methods for Ultra-Wide Band indoor localization using robotic fingerprinting in complex environments




Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

Z. Talebpour; A. Martinoli 

IEEE Robotics And Automation Letters. 2019-10-01. Vol. 4, num. 4, p. 3790-3797. DOI : 10.1109/LRA.2019.2926966.


A Framework for Cooperative Human-Aware Navigation and Coordination of Multi-Robot Systems in Social Environments

Z. Talebpour / A. Martinoli (Dir.)  

Lausanne, EPFL, 2018. 

Risk-Based Human-Aware Multi-Robot Coordination in Dynamic Environments Shared with Humans

Z. Talebpour; A. Martinoli 

2018. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1-5 October, 2018. p. 3365-3372. DOI : 10.1109/IROS.2018.8593586.

Multi-Robot Coordination in Dynamic Environments Shared with Humans

Z. Talebpour; A. Martinoli 

2018. IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Queensland, Australia, May 21-25, 2018. p. 4593-4600. DOI : 10.1109/ICRA.2018.8460978.


Market-based Coordination in Dynamic Environments Based on Hoplites Framework

Z. Talebpour; S. Savarè; A. Martinoli 

2017. The 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, British Columbia, Canada, September 24–28, 2017. p. 1105-1112. DOI : 10.1109/IROS.2017.8202281.

Automatic Calibration of Ultra Wide Band Tracking Systems Using A Mobile Robot: A Person Localization Case-study

A. Canepa; Z. Talebpour; A. Martinoli 

2017. The International Conference on Indoor Positioning and Indoor Navigation (IPIN 2017), Sapporo, Japan, September 18-21, 2017. DOI : 10.1109/IPIN.2017.8115905.

Optimal Path Planning and Coverage Control for Multi-Robot Persistent Coverage in Environments with Obstacles

J. M. Palacios-Gasos; Z. Talebpour; E. Montijano; C. Sagues; A. Martinoli 

2017. International Conference on Robotics and Automation, Singapore, May 29- June 3, 2017. p. 1321-1327. DOI : 10.1109/ICRA.2017.7989156.


Incorporating Perception Uncertainty in Human-Aware Navigation: A Comparative Study

Z. Talebpour; D. Viswanathan; R. Ventura; G. Englebienne; A. Martinoli 

2016. the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), NewYork city, NewYork, USA, AUGUST 26 – 31, 2016. p. 570-577. DOI : 10.1109/ROMAN.2016.7745175.


On-Board Human-Aware Navigation for Indoor Resource-Constrained Robots: A Case-Study with the Ranger

Z. Talebpour; I. Navarro Oiza; A. Martinoli 

2015. 2015 IEEE/SICE International Symposium on System Integration, Nagoya, Japan, December 11-13, 2015. p. 63-68. DOI : 10.1109/SII.2015.7404955.


A Comparison of PSO and Reinforcement Learning for Multi-Robot Obstacle Avoidance

E. Di Mario; Z. Talebpour; A. Martinoli 

2013. IEEE Congress on Evolutionary Computation, Cancún, México, June 20-23, 2013. p. 149-156. DOI : 10.1109/CEC.2013.6557565.