Técnicas de prevención de colisiones y agrupación de flujos

Collision Avoidance on UAV Using Neural Network Pipelines and Flow Clustering Techniques
Drone technology undergoes continuous advancement, with safety standards consistently at the forefront of each progression. A fundamental component of integrating drones into urban landscapes is their capacity to navigate obstacles, ultimately minimizing collisions and ensuring seamless operations. The UAV industry is actively proposing solutions to mitigate potential hazards, and we would like to present our research contribution – solution in this critical matter.

Delving Into the Challenge- Why is collision avoidance so crucial?

Airborne drones face numerous challenges, particularly in navigating moving objects, which demand a sophisticated approach for effective resolution. Objects such as other UAVs, debris, or birds can pose significant obstacles, requiring swift detection to avert collisions and mitigate potentially severe accidents. Industries necessitating precise aerial operations and navigating complex environments rely heavily on collision avoidance systems. For instance, operations conducted within congested construction sites demand stringent safety measures to ensure accident-free aerial inspections. Similarly, drone delivery services, particularly in densely populated urban areas with congested airspace, require advanced safety protocols to operate safely and efficiently.
Resultados de la investigación sobre prevención de colisiones

Current Measures to Address Collision Risks

Presently, technological solutions and regulatory measures have notably mitigated the risk of collision in UAVs. Nonetheless, they might not be entirely sufficient to ensure the highest safety standards. Factors like sensor accuracy, the velocity of moving objects, and human errors during piloting, along with regulatory loopholes, can undermine the efficacy of these measures. Moreover, the advent of new technology introduces additional safety concerns, as every innovation brings along its own set of risks. For instance, the proliferation of autonomous drones and urban air mobility vehicles adds complexity to airspace management and collision avoidance. Consequently, continuous efforts are imperative to address existing limitations and adapt to evolving risks effectively.

Challenges in current measures

While technological solutions and regulatory measures have indeed made strides in addressing collision risks in UAVs, several challenges persist:

The Collision Avoidance Algorithm

Amidst these challenges, our research suggests a solution to transform collision avoidance in UAVs. At the core of our innovation lies the Collision Avoidance Algorithm, a sophisticated fusion of Neural Network Pipeline (NNP) components. This advanced algorithm incorporates Convolutional Neural Network (CNN), Recursive Neural Network (RNN), and Feed-forward Neural Network (FNN) elements to enable swift detection and response to dynamic obstacles encountered during flight.

Utilizing Object Trajectory Estimation (OTE) Algorithm

To establish an efficient framework for collision prevention, our solution partners with the Object Trajectory Estimation (OTE) algorithm, utilizing optical flow analysis. This strategic collaboration boosts the precision of collision avoidance and aids in generating a comprehensive dataset encompassing various scenarios. This dataset enriches research on collision avoidance, paving the path for future advancements in UAV safety technology.
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Advancements by the Collision Avoidance Algorithm and OTE Algorithm

Resumen

Collision avoidance is crucial for seamless drone integration into urban landscapes, minimizing risks and ensuring efficient operations. While current measures have made progress in mitigating collision risks, challenges persist, including technological limitations and regulatory gaps. Our research offers a solution—a sophisticated Collision Avoidance Algorithm, combined with the Object Trajectory Estimation algorithm. Through innovative neural network components and optical flow analysis, our solution aims to enhance collision avoidance capabilities, contributing to safer UAV operations. Moving forward, ongoing innovation and collaboration are vital to address evolving challenges and ensure the safe integration of drones into our airspace.

Key Points

Preguntas relacionadas

¿Cómo han abordado las soluciones tecnológicas y las medidas reguladoras actuales los riesgos de colisión en los UAV?

Aunque han progresado, factores como la precisión de los sensores, los errores humanos y las lagunas normativas socavan su eficacia, lo que pone de relieve la necesidad de una mejora continua.

¿Cuáles son los componentes básicos del algoritmo anticolisión propuesto en la investigación?

El algoritmo incorpora elementos CNN, RNN y FNN dentro de un conducto de red neuronal para detectar y responder a los obstáculos dinámicos encontrados durante el vuelo.

¿Cómo mejora la capacidad anticolisión la asociación con el algoritmo de Estimación de la Trayectoria del Objeto (OTE)?
Al utilizar el análisis del flujo óptico, el algoritmo OTE mejora la precisión en la evitación de colisiones y contribuye a generar un amplio conjunto de datos para futuras investigaciones y mejoras.

Prof. José Fonseca

FCT | Universidad NOVA de Lisboa

Dr. João P. Matos-Carvalho

Universidad Lusófona, COPELABS

Dr. Dário Pedro

CEO y Jefe de equipo de software @ BV

Dr. André Mora

FCT | Universidad NOVA de Lisboa

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