UAV Control System with Time to Collision (TTC) Prediction Capability

Abstract

This paper presents the development of an Unmanned Aerial Vehicle (UAV) control system simulation with collision avoidance prediction capability using the Time-to-Collision (TTC) model. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking control system when the throttle is fully released. Flight mission data collected from the quadcopter testbed platform experiments in the real environment in order to develop TTC model. The horizontal ground speed, throttle magnitudes, and flight time stamp are downloaded from the onboard quadcopter, filtered, analyzed, and optimize using Particles Swarm Optimization (PSO) algorithm to find the optimal TTC model. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their current parameters, for instance, current speed and payload. This development of UAV’s control system implemented in Matlab/Simulik. The PID-based controller is utilized to stabilize the quadcopter and collision avoidance control systems with the TTC model to assist the system in order to avoid a collision from happening. Simulation tests performed proved the capability of UAV to stop at a safe distance and avoid collisions with the obstacles that existed based on TTC model prediction during flight successfully.