基于BI-APF-RRT与仿射变换的复杂环境下多无人机编队协同避障与自适应形状变形控制
Multi-UAV Formation Cooperative Obstacle Avoidance and Adaptive Shape Deformation Control in Complex Environments Based on BI-APF-RRT and Affine Transformation
AI总结：
针对复杂障碍环境下多无人机编队避障灵活性与编队完整性难以共存及传统APF易陷入局部最优的问题，提出一种融合BI-APF-RRT与仿射变换的协同避障算法，实现全局路径规划与自适应变形。
摘要：
针对复杂障碍环境下多无人机编队运动中避障灵活性与编队完整性难以共存，以及传统人工势场（APF）方法易陷入局部最优的问题，提出了一种融合BI-APF-RRT与仿射变换的多无人机编队协同避障算法。首先，摒弃传统的APF质心路径规划方法，采用目标偏向的双向人工势场RRT（BI-APF-RRT）算法，对领航编队质心进行全局无碰撞路径规划。通过引入改进的人工势场和三次B样条插值，保证了全局路径的平滑性和快速收敛性。其次，以生成的全局路径作为编队质心的引导轨迹，结合仿射变换矩阵（包括非均匀缩放和旋转），使编队在沿最优路径运动时能够根据与障碍物的距离自适应变形。最后，跟随者通过分布式控制律跟踪领航者，使整个编队在不解散的情况下安全穿越复杂障碍区域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
