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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Un marco de Q-learning consciente de la energía para la planificación integral de trayectorias de cobertura en

Yao Xue1, Chee Keong Tan2,3, Wai Peng Wong2

  • 1School of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, 47500, Malaysia. yao.xue@monash.edu.

Scientific reports
|December 30, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Desarrollamos PERM-QN, un algoritmo inteligente de planificación de trayectorias para robots. Este método mejora la eficiencia de búsqueda y rescate en zonas de desastre impredecibles al garantizar una cobertura completa del área y la conciencia de la energía.

Palabras clave:
Entornos complejos y desconocidosGasto energéticoPlanificación de trayectorias de cobertura completaAprendizaje por refuerzo

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Área de la Ciencia:

  • Robótica y Sistemas Inteligentes
  • Inteligencia Artificial
  • Operaciones de Búsqueda y Rescate

Sus antecedentes:

  • Los algoritmos convencionales de planificación de trayectorias de cobertura (CPP) enfrentan limitaciones en entornos dinámicos e impredecibles posteriores a desastres.
  • La planificación de trayectorias robóticas requiere adaptabilidad para operaciones eficientes de búsqueda y rescate en terrenos inciertos.

Objetivo del estudio:

  • Introducir PERM-QN (Q-learning con repetición de experiencia prioritaria y red de memoria), un algoritmo inteligente de planificación de trayectorias.
  • Permitir la cobertura completa del área consciente de la energía para robots en entornos complejos de desastres cargados de obstáculos.

Principales métodos:

  • PERM-QN integra una función de recompensa de peso dinámico para un equilibrio adaptativo de la cobertura, la longitud de la trayectoria y el consumo de energía.
  • El mecanismo de repetición de experiencia prioritaria acelera la convergencia del aprendizaje al priorizar experiencias pasadas de alto valor.
  • Una red de memoria acelera la planificación de rutas en regiones de terreno similares, reduciendo la exploración redundante.

Principales resultados:

  • PERM-QN demostró una exploración de área más eficiente y completa en comparación con los métodos tradicionales en entornos simulados posteriores a desastres.
  • El algoritmo mantuvo un rendimiento sólido en diversas complejidades ambientales.
  • PERM-QN logró una cobertura eficaz y consciente de la energía en terrenos inciertos.

Conclusiones:

  • PERM-QN ofrece una solución eficaz para la planificación de trayectorias robóticas en escenarios complejos y dinámicos posteriores a desastres.
  • El algoritmo inteligente mejora las capacidades de los robots en misiones de búsqueda y rescate.
  • Este enfoque mejora la eficiencia y la integridad de la exploración al tiempo que gestiona el consumo de energía.