Decentralized Scalable Exploration via Emergent Adaptive Lévy Walks on Minimal-Sensing Platforms

• URL: http://arxiv.org/abs/2607.25195v1
• Date: Tue, 28 Jul 2026 01:53:28 GMT
• Title: Decentralized Scalable Exploration via Emergent Adaptive Lévy Walks on Minimal-Sensing Platforms
• Abstract summary: We present a lightweight sensor-driven Lévy walk controller for aerial robots weighing under 50 grams.<n>The method combines discrete Lévy step-length sampling with a sensor-reactive heading policy using directional range measurements.<n> Simulation results show coverage improvements of 79.6% in open arenas, 43.1% in rooms-and-corridors layouts, and 13.6% in cluttered environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient autonomous exploration with palm-sized nano-UAVs remains challenging due to severe limitations in sensing, computation, and flight endurance. We present a lightweight sensor-driven Lévy walk (SDLW) controller for aerial robots weighing under 50 grams and equipped with sparse local sensing. The method combines discrete Lévy step-length sampling with a sensor-reactive heading policy using directional range measurements. Each robot independently samples its Lévy exponent from a uniform prior to diversify exploration without inter-robot communication for exploration control. Each robot then selects headings using a von Mises distribution that biases motion toward open directions while preserving superdiffusive exploration properties. The controller operates at constant computational cost, enabling scalable multi-UAV exploration. Simulation results show coverage improvements of 79.6% in open arenas, 43.1% in rooms-and-corridors layouts, and 13.6% in cluttered environments, with collision reductions of 13.0%, 7.1%, and 1.4%, respectively, relative to a uniform-heading Lévy walk baseline. This work provides a practical framework for scalable multi-robot exploration on minimal-sensing, resource-constrained nano-UAVs.

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