Found 2 results
Review
30 December 2024
Fire-Retardant
Wastepaper Reinforced Waste Polyethylene Composite: A Review
The increase in fire
outbreaks recently and the need for eco-friendly and fire-resistant materials
have inspired a wave of studies, focusing on producing innovative composite
materials with effective fire-resistant properties. This review delves into the
world of fire-resistant wastepaper-reinforced waste polyethylene composites.
Using wastepaper as a strengthening factor in polyethylene matrices, combined
with fire-retardant additives like nanoparticles, introduces a hopeful path for
waste management and improved material properties. This work carefully
considers the combining approaches, physical and mechanical properties,
fire-resistant mechanisms, and environmental impacts of these composites. The
review underscores the possible and potential applications, difficulties, and
prospects of such environmentally friendly materials in various industries.
Understanding these composites’ blending, attributes, and conceivable
utilization is essential for advancing maintainable and fire-safe material innovation
in pursuing a greener future.
Article
02 September 2024Multi-Robot Cooperative Target Search Based on Distributed Reinforcement Learning Method in 3D Dynamic Environments
This paper proposes a distributed reinforcement learning method for multi-robot cooperative target search based on policy gradient in 3D dynamic environments. The objective is to find all hostile drones which are considered as targets with the minimal search time while avoiding obstacles. First, the motion model for unmanned aerial vehicles and obstacles in a dynamic 3D environments is presented. Then, a reward function is designed based on environmental feedback and obstacle avoidance. A loss function and its gradient are designed based on the expected cumulative reward and its differentiation. Next, the expected cumulative reward is optimized by a reinforcement learning algorithm that makes the loss function update in the direction of the gradient. When the variance of the expected cumulative reward is lower than a specified threshold, the unmanned aerial vehicle obtains the optimal search policy. Finally, simulation results demonstrate that the proposed method effectively enables unmanned aerial vehicles to identify all targets in the dynamic 3D airspace while avoiding obstacles.
