Target encirclement by heterogeneous swarms composed of uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs) represents a significant application in multi-agent cooperative operations. With the unified kinematic model for the air-ground heterogeneous multi-agent system (HMAS) established, a finite-time distributed target observer (FTDTO) is first developed to address the issue that not all individuals in the swarm can obtain complete target information. An extended state observer (ESO) is then designed to simultaneously estimate both the agent’s own state and external disturbances, effectively handling uncertainties arising from sensor measurement noise and nonlinear model dynamics. Furthermore, considering the inherent communication challenges and bandwidth limitations in complex air-ground cooperative networks, a dynamic event-triggered mechanism (DETM) is employed. Based on this DETM, an HMAS encirclement control law is developed with adaptive radius contraction capability to prevent target escape. Theoretical analysis demonstrates the stability of the proposed ESO, FTDTO, and control law, and shows that Zeno behavior can be avoided. Simulation experiments are conducted to compare the proposed method with existing approaches, verifying the effectiveness of the proposed method and the target tracking capability with adaptive encirclement formation.

This study proposes a bio-inspired control framework for unmanned aerial vehicle (UAV) swarms, designed to emulate the collective motion phase transitions observed in the homing behavior of pigeon flocks. A second-order self-propelled particle model is established, integrating a self-propulsion term, an interaction potential term, and a key roosting force term inspired by the roosting behavior of pigeons. The framework enables the swarm to dynamically switch between a translational motion phase and a vortex motion phase based on the distance to a designated roost location. Based on the proposed swarm model, theoretical analysis proves the stability property of the specific two motion phases under specific conditions. Numerical simulations validate the stability of the two motion phases, demonstrating that UAV swarms can reliably maintain each phase and execute phase transitions triggered by the roosting force. The proposed framework is able to describe the phase transition behavior in the process of pigeons returning home.

Various intelligent trash cans on market at present have many drawbacks. They are complex, expensive, and have only a few functions. A new type of intelligent garbage classification box is designed, which contains four garbage classification buckets. It has the ability to automatically open or close the trash can when users pass by through infrared sensor, classify the garbage into four categories using Raspberry PI, rotate the platform on which the garbage classification buckets are based to put the garbage in place. In addition, the capacity of the garbage sorting bucket is also monitored by ultrasonic ranging sensors. When the bucket is almost full, the garbage can will compress the garbage below or set off an alarm to warn users. The intelligent garbage sorting box is of much use and possesses a high degree of automation, meeting the needs of the majority of home users. So it has a good market application prospect.