Main Page

From The iroboapp Project
Revision as of 21:18, 27 January 2016 by Iroboappadmin (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search
iroboapp: Design and Analysis of Intelligent Algorithms for Robotic Problems and Applications

New Book

Robot Operating System (ROS): The Complete Reference (Volume 1)

  • Publisher: Springer
  • Series: Studies in Computational Intelligence, Vol. 625
  • Editors: Koubaa, Anis,
  • Edition: 2016 (Volume 1), 728 pages.



December 16, 2014: [Call for Chapters] Springer Book on Robot Operating System (ROS)

Springer Book: Robot Operating System (ROS) - The Complete Reference
Special edition in the “Studies in Systems, Decision and Control” Springer Book Series
Publisher: Springer

  • Anis Koubaa (Prince Sultan University, Saudi Arabia)/(CISTER Research Unit, Portugal)

Important Dates:

  • Abstract Submission Deadline: January 05, 2015
  • Full Chapters Due: March 01, 2015
  • Chapter Acceptance Notification: May 01, 2015
  • Revised Version Due Date: May 21 2014
  • Final Notification: 10 June 2015.
  • Estimated Publication Date: August 2015.

Dec 3, 2014: Waypoints visiting with Asctec FireFly UAV

View 1
The video shows a simple field experiment with Asctec FireFly UAV using the Autopilot Control Software to visit two waypoints in a football field and come back to its initial location.
First, we connect to the UAV through the Xbee module connected to the PC and we enable the GPS mode using the Remote Control. Second, we start a GPS mission, load the map of the football field and specify graphically the GPS locations of the waypoints to be visited. We set two waypoints, one on the center of the field and the second near the goal to the north. Then, we manually raise the UAV at a safe height (e.g. 5 meters) with the remote control, and start the mission through the autopilot control software. The UAV will start visiting each UAV in the specified order and will stay up to three second in each UAV. Finally, we send a command to the UAV so that it comes back to home location. Finally, we manually land the UAV.
We are working towards making a cooperative mission with two UAVs to visit a distinct set of target locations.

See in HD on YouTube (View 2)

Sept 15, 2014: Courier Delivery Application using COROS Architecture with ROS

This video presents a demonstration of the COROS architecture in the context of courier delivery application.

The demo consists of three ROS-enabled Turtlebot robots initially waiting for a mission to execute. A mission for delivery of a courier from Office 4 to Office 2 is sent to all three robots. The three robots negotiates using a market-based task allocation mechanism, and only the robot with the minimum estimated total traveled distance will execute the delivery mission.

Cooperative Robots and Sensor Networks 2014, Published

  • Publisher: Springer
  • Series: Studies in Computational Intelligence, Vol. 554
  • Editors: Koubaa, Anis, Khelil, Abdelmajid (Eds.)
  • Edition: 2014 (Second Edition), 231 pages.


Project Scope

What is iroboapp?

iroboapp project is a research project funded by the National Plan for Sciences, Innovation and Technology (NPST) program in Saudi Arabia, and administratively managed by the Science and Technology Unit of Al-Imam Mohamed bin Saud University and by King AbdulAziz for Sciences and Technology (KACST).
The project spans over a duration of two years (Dec 2012 - Nov 2014). The iroboapp project can be considered as a contribution of the successfully completed rtrack project.


The project addresses optimization problems in robotics applications. In fact, several algorithms have been proposed in the literature to embed the intelligence in the robot behavior so as to efficiently accomplish their tasks. These algorithms spans over a large number of techniques including bio-inspired techniques such as swarm intelligence, including ant colony optimization bees’ algorithms, etc, while others are based on evolutionary algorithms such as genetic algorithms, neural networks, evolutionary programming, etc. This diversity and difference raise the complex challenge of choosing the “best” algorithm for a given robotic application.
For example, considering the classical path planning problem, finding the shortest path can be achieved through different techniques ranging from exact methods, to local search, bio-inspired approaches, etc. The question is:" What would be the best method to solve such a problem?".
The literature presents a vast array of solutions for each robotic optimization problem, however, there is a lack of a comprehensive comparative study between the underlying techniques. This project attempts to address this gap. Thus, our generic problem can be formulated as follows:" Given a particular robotic optimization problem (e.g. path planning, multi-robot task allocation), where different solutions are possible, (1) what is the most appropriate optimization approach to solve the problem, (2) how to design hybrid approaches to improve on existing solutions. "

Applications Contexts

Since the robotic application space is huge, we will put focus on those encompassing two typical problems: (1) path planning, (2) multi-robot task allocation (MRTA). As an instance of path planning problem, the travel salesman (TSP) problem with one/multiple agents will also be considered. In fact, these problems are common for a large number of robotic applications, such as disaster management, search and rescue, industrial manufacturing process, port and warehouse automation.
As target applications, we intend to implement proof-of-concepts prototypes for multi-robots cleaning applications and surveillance applications.


For that purpose, the objective of this project is roughly to

  • study the adequacy and effectiveness of existing intelligent algorithms and techniques for path planning and MRTA problems, and
  • design and analyze new effective algorithms for optimizing the behavior of robotic applications,
  • implement real-world robotic applications using the proposed algorithms to demonstrate their effectiveness and feasibility.

We aims at implementing a multi-robot cleaning application as it encompasses both studied problems and we are able to acquire the required technology to implement it. Also, we aim at implementing an indoor surveillance application where ground robots and aerial vehicles collaborate together to achieve their surveillance mission. This project gathers both fundamental and applied research problems as it deals with NP-Hard problems, and investigates how to deploy and implement them in real-world applications.

Selected Videos

Description: This videos shows an illustration of move and improve mechanism using Webots simulator. Video produced by Omar Cheikhrouhou. Move and Improve is a new market-based multi-robot coordination technique for multiple depot, multiple travel salesmen problem (MD-MTSP). The concept is simple: in the beginning of the mission, a robot moves and attempts to improve its solution by coordination with its neighbor robots. Our approach consists of four main steps: (1) initial target allocation, (2) tour construction, (3) negotiation of conflicting targets, (4) solution improvement.