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The navigational strategies of insects using skylight polarization are interesting for applications in autonomous agent navigation because they rely on very little information for navigation.
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We discuss these knowledge structures, describe the agent's navigation capabilities and present a trace function which is used to assess the simulation results.
The main purpose of a navigation agent is providing a correction for the robot's position and/or heading estimate.
A navigation agent is simply a routine that processes a certain kind of sensor information looking for a certain type of navigation feature.
Once the obstacle is avoided, the priority index is updated, resulting in navigation agent attaining higher priority.
Obstacle avoidance and navigation agents are non-collaborative, achieving their respective sub-goals through collaboration with the steering agent.
The infomaps and infocharts are used as the blueprints and navigation agents for building and developing a web-based subject experts depository and query report system.
For example, obstacle avoidance and navigation agents require the basic actions for controlling the velocity and orientation of the robot.
The position of the feet x a and x b of an agent (in general two navigation points of an agent) has a bounded spatial separation.
This includes the domains of objects (intuitive physics), places (spatial navigation) and agents (intentional actors)." But how do you test these things?
The skilful navigation of an agent familiar with the weird and troubled waters of publishing?
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com