Reliable communication in Micro Air Vehicle (MAV) swarms is challenging in environments, where conventional radio-based methods suffer from spectrum congestion, jamming, and high power consumption. Inspired by the waggle dance of honeybees, which efficiently communicate the location of food sources without sound or contact, we propose a novel visual communication framework for MAV swarms using motion-based signaling. In this framework, MAVs convey information, such as heading and distance, through deliberate flight patterns, which are passively captured by event cameras and interpreted using a predefined visual codebook of four motion primitives: vertical (up/down), horizontal (left/right), left-to-up-to-right, and left-to-down-to-right, representing control symbols (“start”, “end”, “1”, “0”). To decode these signals, we design an event frame-based segmentation model and a lightweight Spiking Neural Network (SNN) for action recognition. An integrated decoding algorithm then combines segmentation and classification to robustly interpret MAV motion sequences. Experimental results validate the framework's effectiveness, which demonstrates accurate decoding and low power consumption, and highlights its potential as an energy-efficient alternative for MAV communication in constrained environments.

Hierarchical Reinforcement Learning (HRL) provides an option to solve complex guidance and navigation problems with high-dimensional spaces, multiple objectives, and a large number of states and actions. The current HRL methods often use the same or similar reinforcement learning methods within one application so that multiple objectives can be easily combined. Since there is not a single learning method that can benefit all targets, hybrid Hierarchical Reinforcement Learning (hHRL) was proposed to use various methods to optimize the learning with different types of information and objectives in one application. The previous hHRL method, however, requires manual task-specific designs, which involves engineers’ preferences and may impede its transfer learning ability. This paper, therefore, proposes a systematic online guidance and navigation method under the framework of hHRL, which generalizes training samples with a function approximator , decomposes the state space automatically, and thus does not require task-specific designs. The simulation results indicate that the proposed method is superior to the previous hHRL method, which requires manual decomposition, in terms of the convergence rate and the learnt policy. It is also shown that this method is generally applicable to non-stationary environments changing over episodes and over time without the loss of efficiency even with noisy state information .

A control strategy is proposed to deal with the fundamental gain selection problem of optical flow landings. It involves detecting the height by means of an oscillating movement and setting the control gains accordingly at the start of a landing. Then, during descent, the gains are reduced exponentially, with mechanisms in place to ensure high-performance landings. Real-world experiments with a quadrotor demonstrate successful landings in both indoor and outdoor environments.

We introduce a novel setup of self-supervised learning (SSL), in which optical flow provides the supervised outputs. Optical flow requires significant movement for obstacle detection. The main advantage of the introduced method is that after learning, a robot can detect obstacles without moving - reducing the risk of collisions in narrow spaces. We investigate this novel setup of SSL in the context of a Micro Air Vehicle (MAV) that needs to select a suitable landing place. Initially, when the MAV flies over a potential landing area, the optical flow processing estimates a ‘surface roughness’ measure, capturing whether there are obstacles sticking out of the landing surface. This measure allows the MAV to select a safe landing place and then land with other optical flow measures such as the divergence. During flight, SSL takes place. For each image a texton distribution is extracted (capturing the visual appearance of the landing surface in sight), and mapped to the current roughness value by a linear regression function. We first demonstrate this principle to work with offline tests involving images captured on board an MAV, and then demonstrate the principle in flight. The experiments show that the MAV can land safely on the basis of optical flow. After learning it can also successfully select safe landing spots in hover. It is even shown that the appearance learning allows the pixel-wise segmentation of obstacles.