Efficient and inefficient ant coverage methods
Annals of Mathematics and Artificial Intelligence
Divide and conquer: false-name manipulations in weighted voting games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Coalition game-based distributed coverage of unknown environments by robot swarms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
On redundancy, efficiency, and robustness in coverage for multiple robots
Robotics and Autonomous Systems
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multi-agent coalition formation for distributed area coverage
CARE@AI'09/CARE@IAT'10 Proceedings of the CARE@AI 2009 and CARE@IAT 2010 international conference on Collaborative agents - research and development
Adaptive multi-robot team reconfiguration using a policy-reuse reinforcement learning approach
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Hi-index | 0.00 |
In the multi-robot area coverage problem, a group of mobile robots have to cover an initially unknown environment using a sensor or coverage tool attached to each robot. Multi-robot area coverage is encountered in many applications of multi-robot systems including unmanned search and rescue, aerial reconnaissance, robotic demining, automatic lawn mowing, and inspection of engineering structures. We envisage that multi-robot coverage can be performed efficiently if robots are coordinated to form small teams while covering the environment. In this paper, we use a technique from coalitional game theory called a weighted voting game that allows each robot to dynamically identify other team members and form teams so that the efficiency of the area coverage operation is improved. We propose and evaluate a novel technique of computing the weights of a weighted voting game based on each robot's coverage capability and finding the best minimal winning coalition (BMWC). We theoretically prove the feasibility of our model, and give algorithms to find the BMWC as well. We have also evaluated the performance of our algorithms within a robot simulation platform using up to 40 robots.