Alert button
Picture for Inmo Jang

Inmo Jang

Alert button

Omnipotent Virtual Giant for Remote Human-Swarm Interaction

Apr 01, 2019
Inmo Jang, Junyan Hu, Farshad Arvin, Joaquin Carrasco, Barry Lennox

Figure 1 for Omnipotent Virtual Giant for Remote Human-Swarm Interaction
Figure 2 for Omnipotent Virtual Giant for Remote Human-Swarm Interaction
Figure 3 for Omnipotent Virtual Giant for Remote Human-Swarm Interaction
Figure 4 for Omnipotent Virtual Giant for Remote Human-Swarm Interaction

This paper proposes an intuitive human-swarm interaction framework inspired by our childhood memory in which we interacted with living ants by changing their positions and environments as if we were omnipotent relative to the ants. In virtual reality, analogously, we can be a super-powered virtual giant who can supervise a swarm of mobile robots in a vast and remote environment by flying over or resizing the world and coordinate them by picking and placing a robot or creating virtual walls. This work implements this idea by using Virtual Reality along with Leap Motion, which is then validated by proof-of-concept experiments using real and virtual mobile robots in mixed reality. We conduct a usability analysis to quantify the effectiveness of the overall system as well as the individual interfaces proposed in this work. The results revealed that the proposed method is intuitive and feasible for interaction with swarm robots, but may require appropriate training for the new end-user interface device.

* Submitted to IROS2019. The full demo video is available in https://youtu.be/LOIJPFM8YRA 
Viaarxiv icon

Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

Jul 25, 2018
Inmo Jang, Hyo-Sang Shin, Antonios Tsourdos

Figure 1 for Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
Figure 2 for Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
Figure 3 for Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
Figure 4 for Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System

This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a strongly-connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the solution, and additionally show that 50% of suboptimality can be at least guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation.

* Accepted by IEEE Transactions on Robotics (on 22 May 2018) 
Viaarxiv icon