InMO Lab
Intelligent Multi-robot Orchestration
Intelligent Multi-robot Orchestration
Our mission is to excel research and development in the field of multi-robot systems by leveraging artificial intelligence that enhance teamwork between humans and robots. Our work aims to optimise the collective capabilities of robots in diverse environments, ensuring they can perform complex tasks more efficiently and effectively while seamlessly integrating with human operators.
Our group focuses on in-depth research in artificial intelligence and interaction technologies within the field of multi-robot systems, with an aim towards industrial and real-world applications. As such, our fundamental research is built upon two key pillars: (a) multi-robot intelligence; (b) human multi-robot collaboration. Based on them, we are very keen on pioneering practical applicational research, including but not limioted to infrastructure inspection and cooperative intelligent transportation systems.
This research initiative focuses on designing cutting-edge algorithms that significantly enhance the intelligence and collaborative efficiency of multiple robots. To this end, we utilise game theories, bio-inspired approaches, stochastic methods, optimisation, reinforcement learning, etc.
Related Publications
W Liu, H Niu, I Jang, G Herrmann, J Carrasco, Distributed neural networks training for robotic manipulation with consensus algorithm, IEEE Transactions on Neural Networks and Learning Systems, 2022
J Hu, P Bhowmick, I Jang, F Arvin, A Lanzon, A decentralized cluster formation containment framework for multirobot systems, IEEE Transactions on Robotics, 37 (6), 1936-1955, 2021
A Dutta, V Ufimtsev, T Said, I Jang, R Eggen, Distributed hedonic coalition formation for multi-robot task allocation, IEEE International Conference on Automation Science and Engineering (CASE), 2021
I Jang, HS Shin, A Tsourdos, J Jeong, S Kim, J Suk, An integrated decision-making framework of a heterogeneous aerial robotic swarm for cooperative tasks with minimum requirements, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233 (6), 2101-2118, 2019
I Jang, HS Shin, A Tsourdos, Anonymous hedonic game for task allocation in a large-scale multiple agent system, IEEE Transactions on Robotics, 34 (6), 1534-1548, 2018
I Jang, HS Shin, A Tsourdos, Local information-based control for probabilistic swarm distribution guidance, Swarm Intelligence, 12 (4), 327-359, 2018
I Jang, HS Shin, A Tsourdos, A comparative study of game-theoretical and markov-chain-based approaches to division of labour in a robotic swarm, IFAC NAASS Workshop, 2018
I Jang, HS Shin, A Tsourdos, A Game-theoretical Approach to Heterogeneous Multi-Robot Task Assignment Problem with Minimum Workload Requirements, Workshop on Research, Education and Development of Unmanned Aerial Systems, 2017
HS Shin, I Jang, A Tsourdos, Frequency channel assignment for networked UAVs using a hedonic game, Workshop on Research, Education and Development of Unmanned Aerial Systems, 2017
I Jang, J Jeong, HS Shin, S Kim, A Tsourdos, J Suk, Cooperative Control for a Flight Array of UAVs and an Application in Radar Jamming, IFAC World Congress, 2017
As part of our research infrastructure, we are developing SPACE, a lightweight, Python-based simulator for fast and flexible testing of multi-robot decision-making algorithms. The simulator is being designed with open-source accessibility and academic usability in mind.
Documentation: https://space-simulator.rtfd.io/
This research initiative focuses on optimising intuitive human-robot collaboration to tackle complex tasks by harnessing their combined strengths.
Related Publications
I Jang, J Hu, F Arvin, J Carrasco, B Lennox, Omnipotent virtual giant for remote human–swarm interaction, IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2021
I Jang, H Niu, EC Collins, A Weightman, J Carrasco, B Lennox, Virtual kinesthetic teaching for bimanual telemanipulation, IEEE/SICE International Symposium on System Integration (SII), 2021
I Jang, J Carrasco, A Weightman, B Lennox, Intuitive bare-hand teleoperation of a robotic manipulator using virtual reality and leap motion, Towards Autonomous Robotic Systems (TAROS), 2019
This research initiative is dedicated to advancing robotic inspection technologies to ensure safer industrial infrastructure.
Related Publications
S Han, I Jang, K Kim, H Park, S Hwang, Mobile robot and unmanned inspection system and method including the same, US Patent App. 2024
I Jang, S Lim, H Jeon, Image processing device for drone, drone image processing method, and drone image processing processor, US Patent App. 2023
I Jang, S Lim, H Jeon, Change detection in unmanned aerial vehicle images for industrial infrastructure rooftop monitoring, International Conference on Ubiquitous Robots (UR), 2022
This research initiative focuses on leveraging our expertise in multi-robot intelligence to revolutionise the cooperative intelligent transportation sector.
Related Publications
S Bae, I Jang(corr), S Gros, B Kulcsár, J Hellgren, A game approach for charging station placement based on user preferences and crowdedness, IEEE Transactions on Intelligent Transportation Systems, 23 (4), 3654-3669, 2022
[Nov 2024] Congrats! Jaeho Kim won the Grand Award from 2024 Lockheed Martin Falcon Challenger for his research about multi-robot delivery task allocation!
[Aug 2024] a new undergraduate researcher (김재호) has joined our team.
[Apr 2024] INMO Lab has recently opened, and three new undergraduate researchers (장민지, 강대원, 이선빈) have joined our team.