In context
As low-altitude aircraft technology advances, low-altitude economy has become a strategic emerging industry in China, driving interest in UAV swarms for military and civilian applications. However, fixed formation sizes in enclosing tasks often allow targets to escape, prompting research into adaptive strategies.
What was reported
Researchers from Beihang University proposed a moving target enclosing control framework for UAV swarms based on an improved pigeon-inspired optimization (PIO) algorithm. The framework introduces a variable formation strategy that adjusts the size and altitude of the enclosing formation in response to target speed changes, preventing escape.
The improved PIO algorithm modifies weight factors in both optimization stages: adjusting search direction weights in the map and compass phase, and tuning position update weights for landmark center and familiar pigeons in the landmark phase. This overcomes traditional PIO's tendency to fall into local optima.
Simulation tests showed that the UAV swarm successfully enclosed moving targets while achieving adaptive formation changes. Compared with traditional bio-inspired algorithms, the improved PIO converged faster and found better solutions, enhancing control quality.
"The improved PIO algorithm can quickly converge and find better solutions, thereby enhancing the control quality of UAV swarm in enclosing."
Why it mattered
This work addresses a practical limitation in distributed bearing-based formation control, where fixed geometries reduce effectiveness against agile targets. By enabling adaptive formation scaling and optimizing control parameters, it supports more robust autonomous operations in dynamic environments, relevant for surveillance and interception missions.
Source: 《机器人》期刊 (robot.sia.cn) · Published 2025-06-09 · “基于变权重改进鸽群优化的无人机集群围捕控制”