Electroreceptive Gradient-Following System for Autonomous Drone Landing

Autonomous landing for small aerial vehicles remains difficult in environments where GPS signals weaken and optical systems fail due to fog, dust, smoke, or low illumination. Bees offer a biological model for reliable landing through their ability to sense weak electric fields generated between their charged bodies and floral surfaces. Inspired by this mechanism, this study develops a biomimetic landing method that uses electric field measurements and estimated field gradients to guide a drone toward a landing target. The approach models the interaction between a charged surface and a vehicle equipped with hair-inspired electroreceptive sensors, then reconstructs the local field structure using a gradient estimator. A gradient–vector–alignment controller drives the vehicle toward regions of stronger electric field while stabilizing its lateral motion during descent. The system is evaluated in simulation under realistic environmental conditions that include sensor noise, wind disturbances, and electromagnetic interference. Results show that electric-field-based sensing combined with gradient-driven control enables reliable landing in situations where traditional vision-based or beacon-based methods become unreliable. This work establishes the foundation for future physical testing and integration of electroreceptive sensors on small drones.