Visual Autonomous Quadrotor Navigation Using an Improved Artificial Potential Field

In this study, perception, planning, and control are integrated for the visual navigation of an autonomous quadrotor in an uncharted global map. To estimate the quadrotor's pose, a visual-inertial odometry strategy is employed. Then, the semi-global matching technique is applied in the stereo vision algorithm to produce an occupancy grid, upon which both motion planning and control tasks are performed. From a comprehensive literature review, the artificial potential field method emerged as the preferred choice for motion planning due to its ability to operate in unknown global maps. To address the inherent issue of local minima, a novel solution is introduced and tested. The often observed oscillations of the artificial potential field are mitigated through a combination of minimum jerk waypoint navigation and potentials, proving to be noticeably more efficient than conventional sample-based approaches like the rapidly exploring random tree. Finally, a state feedback differential flatness-based controller is proposed based on the literature. When integrated with the generated occupancy grid and the enhanced potential field in numerical simulations, the quadrotor navigated autonomously through an unknown global map, efficiently maneuvering through narrow passages without oscillations, adeptly escaping from local minima, and reaching a goal with obstacles nearby.