Tiny flying insects rely heavily on optical flow for landings and navigation. By maintaining a constant optical flow divergence, they can approach targets or obstacles with an exponential decay of both relative distance and velocity. Previous studies have shown that Micro Air Vehicles (MAVs) leverage this control strategy for efficient landings and safe obstacle approaches. However, a key question remains in both biological systems and MAVs: when is the moment to extend an insect’s legs or turn off the motor for landing, or to stop in front of obstacles? To address this, we propose a method that utilizes visual appearance cues to make these decisions. Our approach extracts visual features using texton distribution and employs the chi‐square test with data‐driven adaptive thresholds to detect when an MAVs should halt and what visual appearance characterizes an obstacle. Several flight tests with varying flow divergence setpoints validate that this method ensures smooth obstacle approaches while effectively determining a safe stopping point. Additionally, the results show that MAVs can segment obstacles from a distance using the detection results. A potential application of this approach is in swarm navigation, where sharing minimal, onboard‐processed visual cues allows MAVs to efficiently predict obstacle size in advance, which facilitates coordinated path planning and collision avoidance. By leveraging cooperative perception strategies, this could significantly enhance the autonomy and efficiency of MAVs swarms in complex environments.
Shi et al. (Thu,) studied this question.