arXiv:2512.18333v2 Announce Type: replace-cross Abstract: This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors. With the literature focusing on controlling the four rotors' RPMs directly, this paper aims to control the quadrotor's thrust vector. The RL agent computes the percentage of overall thrust along the quadrotor's z-axis along with the desired Roll ($\phi$) and Pitch ($\theta$) angles. The agent then sends the calculated control signals along with the current quadrotor's Yaw angle ($\psi$) to an attitude PID controller. The PID controller then
The continuous advancements in sophisticated reinforcement learning algorithms like SAC are enabling more precise and adaptable control systems for robotics.
This development indicates a growing capability to imbue autonomous systems with more nuanced and robust control, reducing reliance on conventional, less adaptive methods.
Traditional PID control systems for robotics are being augmented or replaced by adaptive AI agents, leading to more resilient and efficient operational capabilities.
- · Robotics manufacturers
- · Logistics and delivery sectors
- · Search and rescue operations
- · Academic AI researchers
- · Developers of legacy control systems
- · Industries resistant to AI integration
Quadrotors will achieve higher precision and adaptability in various environments, including turbulent or unpredictable conditions.
This improved control will accelerate the deployment of autonomous drones in complex tasks, potentially reducing human intervention in hazardous or repetitive operations.
Sophisticated RL-based control could become a standard for autonomous aerial vehicles, driving further innovation in drone design and application, influencing future regulations for autonomous airspace usage.
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