Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment

TL;DR

This paper introduces a novel UAV swarm formation motion planning framework that combines perception sharing, active sensing, and global optimization to enhance obstacle avoidance in dense environments.

Contribution

It proposes a perception-shared, swarm trajectory global optimal algorithm fused with active sensing and affine formation constraints, improving safety and obstacle avoidance success rates.

Findings

Achieves at least 80% success rate in obstacle avoidance in simulations.

Active sensing increases obstacle avoidance success from 50% to 100% in some scenarios.

Ensures strict consensus among UAVs using GMM communication and STGO.

Abstract

This paper proposes a perception-shared and swarm trajectory global optimal (STGO) algorithm fused UAVs formation motion planning framework aided by an active sensing system. First, the point cloud received by each UAV is fit by the gaussian mixture model (GMM) and transmitted in the swarm. Resampling from the received GMM contributes to a global map, which is used as the foundation for consensus. Second, to improve flight safety, an active sensing system is designed to plan the observation angle of each UAV considering the unknown field, overlap of the field of view (FOV), velocity direction and smoothness of yaw rotation, and this planning problem is solved by the distributed particle swarm optimization (DPSO) algorithm. Last, for the formation motion planning, to ensure obstacle avoidance, the formation structure is allowed for affine transformation and is treated as the soft constraint on the control points of the B-spline. Besides, the STGO is introduced to avoid local minima. The combination of GMM communication and STGO guarantees a safe and strict consensus between UAVs. Tests on different formations in the simulation show that our algorithm can contribute to a strict consensus and has a success rate of at least 80% for obstacle avoidance in a dense environment. Besides, the active sensing system can increase the success rate of obstacle avoidance from 50% to 100% in some scenarios.