Fast Cooperative Flight of UAV Swarms in GNSS-denied and Feature-poor Environments without Explicit Communication

Основные понятия

A decentralized swarm framework is proposed to enable fast cooperative flight of UAVs in GNSS-denied and feature-poor environments without relying on explicit communication.

Аннотация

The proposed swarm framework consists of several interconnected modules:

1. Model and State Estimation of Surroundings: A bank of Linear Kalman Filters is used to model and estimate the state of observable UAVs (oUAVs) in the swarm. This provides a reliable neighborhood model for the high-level control.

2. Flocking Control: A state feedback control law is designed to specify the desired group velocity and stabilize each UAV in an unambiguous position within the swarm formation. This approach allows for significantly higher swarm velocities compared to standard reactive flocking algorithms.

3. Enhanced Multi-Robot State Estimation (MRSE): The onboard state estimation is enhanced by adaptively fusing Visual Inertial Odometry (VIO) with the cooperative state estimation. This improves the reliability of the purely onboard localization in feature-poor environments.

4. Velocity Estimation of Observable UAVs: To decrease the dependence on unreliable communication networks, a method is introduced to estimate the velocities of neighboring UAVs based on the observed swarming behavior, without explicit communication.

The proposed framework was extensively validated through complex real-world experiments, demonstrating its capability to achieve high group velocities up to the physical limits of the hardware, while maintaining swarm cohesion without reliance on GNSS and communication.

Статистика

The swarm reached a group velocity of 5 m/s while maintaining an average distance between neighboring UAVs of 15.12 m with a standard deviation of 5.07 m.

Цитаты

"The proposed distributed state feedback flocking controller allows to stabilize each UAV in an unambiguous position within the constellation and specify the desired collective velocity. As a result, the designed approach allows the swarm to fly at a significantly higher speed compared to existing approaches."
"The incorporation of enhanced MRSE allows for safe deployment of the swarm in challenging conditions of environments with a low amount of visual features and no GNSS data available, where standard techniques of onboard localization fail."

Ключевые выводы из

Fast Swarming of UAVs in GNSS-denied Feature-poor Environments without Explicit Communication

by Jiri Horyna,... в arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18729.pdf

Дополнительные вопросы

How could the proposed framework be extended to handle dynamic obstacles or moving targets in the environment?

The proposed framework could be extended to handle dynamic obstacles or moving targets by incorporating dynamic path planning algorithms. By integrating real-time obstacle detection and tracking mechanisms, the UAV swarm can adjust its flight path to avoid collisions with dynamic obstacles or to effectively track moving targets. This would involve updating the neighborhood model and adjusting the flocking control rules based on the dynamic nature of the obstacles or targets. Additionally, implementing predictive algorithms that anticipate the future positions of moving targets can enhance the swarm's ability to follow and track them effectively.

What are the potential limitations or failure modes of the velocity estimation approach without explicit communication, and how could they be addressed?

One potential limitation of the velocity estimation approach without explicit communication is the accuracy of the estimated velocities of neighboring UAVs. In scenarios where the observed UAVs' movements are complex or unpredictable, the estimation errors can accumulate, leading to inaccuracies in the desired velocities and potentially affecting the overall swarm behavior. To address this, the estimation algorithm could be enhanced by incorporating machine learning techniques to improve the prediction of velocities based on historical data and patterns. Additionally, implementing redundancy in the estimation process by combining multiple estimation methods or sensor modalities can help mitigate errors and enhance the robustness of the velocity estimation approach.

What insights from biological swarm systems could be further incorporated to enhance the adaptability and robustness of the proposed artificial swarm framework?

Incorporating insights from biological swarm systems can enhance the adaptability and robustness of the proposed artificial swarm framework. One key insight is the concept of decentralized decision-making and self-organization observed in natural swarms. By allowing individual UAVs to make autonomous decisions based on local information and interactions with neighboring agents, the swarm can adapt to changing environmental conditions and maintain cohesion without centralized control. Additionally, mimicking the communication mechanisms found in biological swarms, such as pheromone trails or visual cues, can enhance the coordination and communication within the artificial swarm. Furthermore, integrating principles of swarm intelligence, such as collective decision-making and task allocation based on emergent behaviors, can improve the overall efficiency and problem-solving capabilities of the UAV swarm.