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This paper introduces Dendritic Recursive Pivoting (DeRP), a decentralized algorithm for forming power-delivery networks in unstructured environments using local communication and bearing-based sensing. By enabling robot swarms to self-assemble power networks from a common source through recursive branching, DeRP approximates Steiner trees to efficiently route power to multiple sinks without requiring global planning. The results show that networks formed by DeRP achieve approximately 125% of the global minimum length while significantly reducing power losses to 65% compared to traditional Euclidean Steiner trees, demonstrating its effectiveness in adaptive power delivery.
Robot swarms can autonomously self-assemble power networks that are 65% more efficient than traditional methods, even in chaotic environments.
Delivering sustained power to distributed equipment in unstructured field environments using pre-planned wired networks or battery-based solutions presents significant infrastructure and logistics challenges. This paper presents Dendritic Recursive Pivoting (DeRP), a decentralized framework for multi-target network formation in robot swarms based solely on local communication and bearing-based sensing toward sinks. We envision a system in which robots, acting as a conduit, self-assemble a power network from a common source, forming branches at locally selected pivot points that approximate the Steiner points of Steiner trees to efficiently route to multiple Sinks. This branching operation is performed recursively to enable scalable and adaptive network formation without global planning. The proposed method is evaluated in terms of the total network length and estimated power loss, and is quantitatively compared against global baselines such as the Minimum Spanning Tree and Steiner tree solutions (GeoSteiner), which require complete knowledge of Sink locations. Specifically, we found that the networks formed by DeRP asymptotically form approximately 125\% of the global minimum length while reducing power losses to 65\% relative to Euclidean Steiner trees. In addition, we empirically characterize scaling behavior by measuring simulation completion time as the number of Sinks and robots increases, and find that this scaling was sub-linear for up to 100 sinks. The proposed approach enables resilient, adaptive power delivery in environments where deployment of traditional infrastructure is challenging.
