Collision Avoidance for Multiple Micro Aerial Vehicles using Fast Centralized Nonlinear Model Predictive Control

Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance

Multiple multirotor Micro Aerial Vehicles (MAVs) sharing the same airspace require a reliable and robust collision avoidance technique. In this paper we address the problem of multi-MAV reactive collision avoidance. A model-based controller is employed to achieve simultaneously reference trajectory tracking and collision avoidance. Moreover, we also account for the uncertainty of the state esti...

Omnidirectional Obstacle Perception and Collision Avoidance for Micro Aerial Vehicles

In this paper, we propose a complete micro aerial vehicle platform—including hardware setup and processing pipeline—that is able to perceive obstacles in (almost) all directions in its surrounding. In order to compensate for deficiencies of individual obstacle sensors, we make use of different sensor modalities. Detected obstacles are fused and accumulated in a three-dimensional egocentric obst...

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

Department of Mechanical & Aerospace Engineering, University of Texas at Arlington, Arlington, TX 76019, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-817-272-7467 † This paper is an extended version of our paper published in the Proceedings of the AIAA Atmospheric Flight Mechanics Conference, AIAA Aviation Forum, Dallas, TX, USA, 22–25 June 2015; Kamesh Subbarao, Ca...

Reactive Collision Avoidance for Unmanned Aerial Vehicles Using Doppler Radar

Research into reactive collision avoidance for unmanned aerial vehicles has been conducted on unmanned terrestrial and mini aerial vehicles utilising active Doppler radar obstacle detection sensors. Flight tests conducted by flying a mini UAV at an obstacle have confirmed that a simple reactive collision avoidance algorithm enables aerial vehicles to autonomously avoid obstacles. This builds up...

Threat-based hazard avoidance for semi-autonomous vehicles using nonlinear model predictive control