Multi-robot informative path planning
A group of autonomous robots can be used for collecting useful information from an environment. The information can range from a simple image/video to the measurement of moisture level in the soil. We are looking at the problem of dispersing the robots in a way such that maximum information from the environment can be collected. Our proposed approach will also handle real-world limitations of the robots such as communication and power constraints.
Project website: http://ayandutta.domains.unf.edu/unf-prec-ag/
Funding Agency: National Science Foundation (NSF), UNF
Related Publications
- Dutta, A., Ghosh, A., & Kreidl, O. P. (2019, May). Multi-robot informative path planning with continuous connectivity constraints. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 3245-3251). IEEE (video).
Robotic exploration under resource constraints
Real-world robots have various resource constraints including limited power supply and communication range. Due to these practical constraints, single/multi-robot exploration planning cannot follow classical techniques. For example, if the power supply is limited, covering all the locations in a large environment (e.g., farmland) becomes prohibitive. We aim to develop techniques for exploration in such scenarios.
Related Publication
- Sharma, G., Dutta, A., & Kim, J. H. (2019, May). Optimal online coverage path planning with energy constraints. In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (pp. 1189-1197). (video).
Coalition formation by multiple robots for task completion
Due to the complex nature of the real-world tasks, multiple robots need to work together towards accomplishing them. We are looking at the problem of partitioning a set of n robots into m coalitions so that these coalitions can be assigned to m tasks. The objective of the robots is to minimize the cost of moving from their initial locations to the positions, where the tasks are located.
Related Publication
- Czarnecki, E., & Dutta, A. (2019, October). Hedonic coalition formation for task allocation with heterogeneous robots. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 1024-1029). IEEE.
Modular Robotics: Configuration Formation & Locomotion Learning
During my Ph.D., I have worked on a NASA-sponsored project called ModRED (Modular Robot for Exploration and Discovery). My dissertation on modular robotics can be found here. I have primarily looked at three fundamental problems in modular robotics:
- how multiple robotic modules make decisions and form coalitions for task completion
- how the modules plan to form user-defined shapes or configurations
- how a modular robot of random shape consisting of multiple robotic modules learns to move from one point to the other.
Related Publications
