Multi-UAV Formation Cooperative Obstacle Avoidance and Adaptive Shape Deformation Control in Complex Environments Based on BI-APF-RRT and Affine Transformation

2026-06-28

AI Summary

This work addresses the challenge of simultaneously ensuring obstacle-avoidance agility and formation integrity for multi-UAV systems operating in complex environments. To this end, a cooperative planning framework is proposed that integrates a target-biased bidirectional artificial potential field–RRT (BI-APF-RRT) with affine transformations. The BI-APF-RRT algorithm generates smooth, rapidly converging global collision-free trajectories while circumventing the local minima commonly encountered in conventional artificial potential field methods. Concurrently, affine transformations incorporating non-uniform scaling and rotation enable adaptive, dynamic reshaping of the formation along the planned paths. A distributed control law further ensures coordinated navigation through cluttered spaces. Experimental results demonstrate that the proposed approach effectively balances collision avoidance safety with formation coherence, significantly enhancing the autonomous navigation capabilities of multi-UAV systems in complex scenarios.

Abstract

Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-UAV formation motion in complex obstacle environments , and that the traditional artificial potential field (APF) method easily falls into local optima, a cooperative obstacle avoidance algorithm for multi-UAV formations integrating BI-APF-RRT and affine transformation is proposed. First, abandoning the traditional APF centroid path planning method , a goal-biased Bidirectional Artificial Potential Field method RRT (BI-APF-RRT) algorithm is adopted to conduct global collision-free path planning for the centroid of the leader formation. By introducing an improved artificial potential field and cubic B-spline interpolation, the smoothness and rapid convergence of the global path are ensured. Secondly, using the generated global path as the guiding trajectory for the formation's centroid , combined with an affine transformation matrix (including non-uniform scaling and rotation) , the formation can adaptively deform based on the distance to obstacles while moving along the optimal path. Finally, the followers track the leaders through a distributed control law , enabling the entire formation to safely cross complex obstacle areas without disassembling.

Problem

• multi-UAV formation
• obstacle avoidance
• formation integrity
• complex environments
• local minima

Innovation

• BI-APF-RRT
• affine transformation
• multi-UAV formation
• adaptive shape deformation
• cooperative obstacle avoidance