UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles

Abstract

Path planning of unmanned aerial vehicles (UAVs) for reconnaissance and look-ahead coverage support for mobile ground vehicles (MGVs) is a challenging task due to many unknowns being imposed by the MGVs’ variable velocity profiles, change in heading, and structural differences between the ground and air environments. Few path planning techniques have been reported in the literature for multirotor UAVs that autonomously follow and support MGVs in reconnaissance missions. These techniques formulate the path planning problem as a tracking problem utilizing gimbal sensors to overcome the coverage and reconnaissance complexities. Despite their lack of considering additional objectives such as reconnaissance coverage and dynamic environments, they retain several drawbacks, including high computational requirements, hardware dependency, and low performance when the MGV has varying velocities. In this study, a novel 3D path planning technique for multirotor UAVs is presented, the enhanced dynamic artificial potential field (ED-APF), where path planning is formulated as both a follow and cover problem with nongimbal sensors. The proposed technique adopts a vertical sinusoidal path for the UAV that adapts relative to the MGV’s position and velocity, guided by the MGV’s heading for reconnaissance and exploration of areas and routes ahead beyond the MGV sensors’ range, thus extending the MGV’s reconnaissance capabilities. The amplitude and frequency of the sinusoidal path are determined to maximize the required look-ahead visual coverage quality in terms of pixel density and quantity pertaining to the area covered. The ED-APF was tested and validated against the general artificial potential field techniques for various simulation scenarios using Robot Operating System (ROS) and Gazebo-supported PX4-SITL. It demonstrated superior performance and showed its suitability for reconnaissance and look-ahead support to MGVs in dynamic and obstacle-populated environments.

Path Planning of Unmanned Aerial Vehicles (UAVs) in Windy Environments

“…In such a scenario, the UAV cannot provide continuous and adequate visual support while following the GMT. Existing path planning, such as G-APFs [24], ED-APFs [25], and D-APFs [12], lack the ability to handle wind effects and thus cannot be used in real-world applications because the realistic environment is amorphous and windy. The proposed path-planning technique solves this problem by modifying the attractive force of the D-APF and ED-APF to enhance the sensitivity and by introducing a standoff distance based on the roll and pitch angles which are sensitive to wind speed and direction and to UAV speed.…”

Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm

“…Nevertheless, several existing solutions have tried utilizing SDN or a multi-objective routing for ground vehicles [ 10 , 11 , 12 ]. Moreover, routing in UAVs which considers metrics such as charging stations, VANET supporting networks, and trajectory-based systems has also been proposed [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. An exhaustive review of existing schemes identified that a routing scheme should not consider a single objective while disregarding its byproduct’s impact.…”

Nonlinear Model Predictive Impedance Control of a Fully Actuated Hexarotor for Physical Interaction

“…Unmanned aerial vehicles (UAVs) have attracted increasing attention in recent years for their wide range of applications, including pesticide spraying [1], aerial photography [2], environmental monitoring [3], reconnaissance [4], and transportation [5]. In addition to these non-contact tasks, there are increasing demands on contact-based operations such as industrial contact inspection [6], window-cleaning [7], peg-in-hole assembly [8], and aerial grasping [9].…”