  In this article we propose a reactive constrained navigation scheme, with
embedded obstacles avoidance for an Unmanned Aerial Vehicle (UAV), for enabling
navigation in obstacle-dense environments. The proposed navigation architecture
is based on Nonlinear Model Predictive Control (NMPC), and utilizes an on-board
2D LiDAR to detect obstacles and translate online the key geometric information
of the environment into parametric constraints for the NMPC that constrain the
available position-space for the UAV. This article focuses also on the
real-world implementation and experimental validation of the proposed reactive
navigation scheme, and it is applied in multiple challenging laboratory
experiments, where we also conduct comparisons with relevant methods of
reactive obstacle avoidance. The solver utilized in the proposed approach is
the Optimization Engine (OpEn) and the Proximal Averaged Newton for Optimal
Control (PANOC) algorithm, where a penalty method is applied to properly
consider obstacles and input constraints during the navigation task. The
proposed novel scheme allows for fast solutions, while using limited on-board
computational power, that is a required feature for the overall closed loop
performance of an UAV and is applied in multiple real-time scenarios. The
combination of built-in obstacle avoidance and real-time applicability makes
the proposed reactive constrained navigation scheme an elegant framework for
UAVs that is able to perform fast nonlinear control, local path-planning and
obstacle avoidance, all embedded in the control layer.
