3D Bioprinting Market to Boom as Researchers Create 3D Print Artificial Heart Valves

Posted On June 16, 2022     

Four heart valves in the human body guarantee that blood flows in the right direction. The appropriate opening and closing of heart valves are critical. Their tissue is heterogeneous, which means that diverse biomechanical qualities exist within the same tissue to complete this function.

Researchers have created 3D printed artificial heart valves that allow the patient's cells to regenerate new tissue. The newly developed fabrication platform constructs these scaffolds using melt electro writing. This advanced additive manufacturing technology allows them to combine several precise, bespoke designs and fine-tune the scaffold's mechanical properties. The development is likely to boost the 3D Bioprinting Market as the long-term goal is to build implants for kids that grow into new tissue and last a lifetime.

The team discovered that terrain geometric elements, such as elevation variations or roughness, substantially impact a robot's movement stability during navigation. As a result, for robots to make safe navigation judgments, they must be able to perceive these topographical elements in the environment.

The present hybrid machine learning architecture combines the intermediate output findings of the attention-based DRL network with a novel trajectory planning strategy. During navigation, these intermediate outcomes aid in identifying and avoiding challenging or dangerous areas in the environment. This is done to create a novel hybrid machine learning architecture. The method incorporates a fully trained DRL network that computes an attention mask using elevation maps, robot pose, and goal as inputs.

The attention mask produced by the team's algorithm then directs a mobile robot to areas in its environment where it should focus its attention to accomplish stable navigation. Finally, this mask is combined with the method's input elevation map to create a 2D navigation cost map. This map is then used to plot a safe and dependable path for the robot to follow to arrive at its destination.

Researchers demonstrated that the unique hybrid formulation combines an attention DRL network for perception and a waypoint planner for navigation. It results in a high navigation success rate on complicated terrains. Thus denoting that when navigating in difficult, uneven terrains, the strategy considerably minimizes the probability of robot flip-overs.

TERP performed well in the team's initial tests, indicating that it has the potential to dramatically increase the reliability of robot navigation in challenging outside conditions. This could be utilized in the future to improve the performance of robots in various situations, such as planetary and space adventures, agricultural surveys, and sophisticated environmental monitoring.

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