Onboard Modular Control and Planning for Autonomous Indoor UAV Navigation on Resource-Constrained CPUs

This paper presents a fully onboard control and planning system for indoor quadrotor navigation, leveraging modular control and lightweight motion planning. Autonomous operation of unmanned aerial vehicles (UAVs) in indoor environments, such as inspection, monitoring, and mapping, faces challenges due to the absence of global navigation satellite system (GNSS) and limited onboard computational resources. Existing methods often rely on external localization systems or high-specification CPUs, restricting deployment on compact, low-power UAVs. To address this, we develop a hierarchical control architecture comprising motor, attitude, velocity, and position controllers, all implemented with full-state feedback. Paired with a custom lightweight motion planning algorithm, the system operates efficiently on resource-constrained CPUs such as the Raspberry Pi. This approach enables affordable, low-power UAVs to achieve autonomous indoor navigation without external infrastructure. The results show stable flight performance and computational efficiency, validating suitability for GNSS-denied environments.