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Learning to Integrate Perception and Power Consumption for path planning in Autonomous Flights
UAV VISUALIZATIONS IN AIRSIM SIMULATION
AirSim Drone Control : Algorithm testing with random walking
UAV Topology Control Airsim Simulation
airsim planning and moving robotic club VSU
AirSim Drone DQN Position
Four Quadrotors in Airsim with Motion Planning using OMPL
A* optimal path planning and drone flight.
Avoidance algorithm test with Airsim
Visual Exploration and Path Planning with Reinforcement learning
DRL for drone in Airsim NH
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Last Updated: September 30, 2026
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Summary
Gitlab Repo: gitlab.com/robotics11/ These results are of the Ain Shams University team. Obstacle detection and avoidance are crucial for modern-day When the task is to explore for oil spills or harmful algae blooms on open water, a flying robot can learn to develop an efficient ... Various Frame Processing from a Action space: Left / Right by position of 2.5 meters State space: Depth map, resized to 64x64 Model: Deep Q Network Reward ...
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