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:
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

1. Ayan Dutta, Prithviraj Dasgupta, and Carl Nelson, Distributed Configuration Formation with Modular Robots Using (Sub)Graph Isomorphism-based Approach“, Springer Autonomous Robots, 2018 (accepted).
2. Ayan Dutta, Prithviraj Dasgupta, Carl Nelson, Adaptive Locomotion Learning in Modular Self-reconfigurable Robots: A Game Theoretic Approach“, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017 (Video).