PACNav: a collective navigation approach for UAV swarms deprived of communication and external localization

Abstract

This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving decentralized collective navigation of Unmanned Aerial Vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts of path persistence and path similarity that allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (1) UAVs with little variation in motion direction have high path persistence, and are considered by other UAVs to be reliable leaders; (2) groups of UAVs that move in a similar direction have high path similarity, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.

MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems

“…Using the UVDAR system, MRS UAVs were able to avoid dynamic objects (predators) that were actively approaching the group. In the same line, [1] proposes an approach for achieving decentralized collective navigation of UAV swarms without communication and without global localization infrastructure. The work addresses several challenges related to the real-world deployment of a swarm of UAVs and their collective motion in a cluttered environment.…”

Compact and ordered swarms of unmanned aerial vehicles in cluttered environments

“…But the BIC swarm does not address the problem of self-excited oscillations that occur when UAVs encounter an obstacle. To address the deadlock problem often suffered by reactive collision avoidance methods based on potential fields, Ahmad et al [24] constructed a collision avoidance vector with parallel and orthogonal directional components relative to the position vector by rotating the matrix. The combined parallel and orthogonal motions make the UAV fly away from and around the obstacle, but the overall order metric of the swarm needs to be further improved.…”

“…To achieve safe and smooth flight of the UAV swarm in a dense obstacle environment, the virtual obstacle method [25] is used to design the obstacle avoidance term, where the obstacle is considered as a virtual agent located along the obstacle boundary. Note that here we do not need to consider the deadlock problem mentioned in [24], since the agents are affected by the interactions between them in addition to the obstacles. The virtual agent is heading perpendicular to the obstacle with a certain speed, v obs (see figure 1).…”

“…Mathematically, the Reynolds rule can be modeled by applying virtual forces related to the virtual potential field to an agent's neighbors. The advantage of the swarm models established by the artificial potential field is that they are purely reactive, and the decision of the agent is completely based on the information within its current perception range, with low computational complexity, which is easy to implement in the real UAV system [23,24]. Olfati-Saber [25] proposed a theoretical framework for the design and analysis of distributed swarm algorithms based on potential function and graph theory, and innovatively considered convex obstacles as virtual agents to deal with swarm obstacle avoidance.…”

Autonomous robotic drone system for mapping forest interiors

“…During the last few years, several open-sourced solutions for inside forest flying have been proposed in the literature. Most of the solutions use either Lidar (Liu et al, 2022;Ren et al, 2022;Liu et al, 2024;Ahmad et al, 2022) or camera (Zhou et al, 2022;Campos-Macías et al, 2021) to create a 3D grid map for collisionfree path trajectory planning and track the position of the drone in local coordinate system. Some of these methods support also autonomous navigation for swarms of drones (Zhou et al, 2022;Ahmad et al, 2022).…”