Hexacopter Weight Lifting Drone

Abstract

The rapid expansion of e-commerce, emergency supply networks, and smart logistics infrastructure has accelerated the demand for reliable heavy-lift autonomous aerial delivery platforms. This paper presents the design, implementation, and performance evaluation of an AI-enabled hexacopter UAV optimized for medium-payload transportation. The proposed system integrates a Pixhawk-based PX4 autopilot, high-thrust BLDC propulsion, and a smart hybrid energy management framework combining Li-Po battery monitoring with predictive energy modeling. An M8N GPS module with sensor fusion-based Extended Kalman Filtering ensures precise waypoint navigation, while an onboard AI-based energy prediction model estimates real-time endurance under dynamic payload conditions.

To enhance operational intelligence, the platform incorporates computer vision-based obstacle detection, LoRa telemetry for long-range communication, adaptive PID tuning, and geofencing-enabled failsafe mechanisms. Experimental evaluation demonstrates stable payload lifting up to 2.5 kg with controlled thrust margins and predictable energy consumption behavior. Flight endurance ranged from 19 minutes (no payload) to 9 minutes (2.5 kg payload), validating thrust-to-weight optimization and power management efficiency.

The developed prototype demonstrates the feasibility of integrating hybrid energy awareness, AI-driven flight optimization, and autonomous mission intelligence for next-generation smart logistics applications, especially in remote and infrastructure-constrained environments.