Simple identical agent systems are becoming more common in nanotechnology, biology, and chemistry. Since, in these domains, each agent can implement only necessarily by simple mechanisms, the major challenge of these systems is how to control the agents using limited control input, such as broadcast control. Inspired by previous work, in which identical agents can be controlled via global inputs using a single fixed obstacle, we propose a new pipeline that uses tree search and matching methods to identify target and agent pairs to move, and their orders. In this work, we compare several matching methods from a hand-crafted template matching to learned feature descriptors matching, and discuss their validity in the pathfinding problem. We also employ the Monte Carlo Tree Search algorithm in order to enhance the efficiency of the tree search. In experiments, we execute the proposed pipeline in shape formation tasks. We compare the total number of control steps and computation time between the different matching methods, as well as against previous work and human solutions. The results show all our methods significantly reduce the total number of input steps compared to the previous work. In particular, the combination of learned feature matching and the Monte Carlo Tree Search algorithm outperforms all other methods.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44777 |
| Date | 24 May 2022 |
| Creators | Negishi, Tomoya |
| Contributors | Tron, Roberto |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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