Installation of ROS and Gazebo

Install mavros and mavlink from source:

cd ~/catkin_ws
wstool init ~/catkin_ws/src

rosinstall_generator --upstream mavros | tee /tmp/mavros.rosinstall
rosinstall_generator mavlink | tee -a /tmp/mavros.rosinstall
wstool merge -t src /tmp/mavros.rosinstall
wstool update -t src
rosdep install --from-paths src --ignore-src --rosdistro `echo $ROS_DISTRO` -y

catkin build

Add a line to end of ~/.bashrc by running the following command:

echo "source ~/catkin_ws/devel/setup.bash" >> ~/.bashrc

update global variables

source ~/.bashrc

install geographiclib dependancy

sudo ~/catkin_ws/src/mavros/mavros/scripts/install_geographiclib_datasets.sh

Install ardupilot :

cd ~
git clone https://github.com/khancyr/ardupilot_gazebo.git
cd ardupilot_gazebo

Ubuntu 18.04 only checkout dev

git checkout dev

build and install plugin

mkdir build
cd build
cmake ..
make -j4
sudo make install

In order to ensure the efficient passage and safety of the inspection UAV in the complex environment, the traditional spherical repulsive field of the artificial potential field method is improved into an ellipsoidal repulsive field, where the UAV velocity direction and the long semi-axis are always co-linear, and the ellipsoidal size is related to the magnitude of the components of the UAV velocity in the X-axis and Y-axis, and the Z-axis component is equal to the Y-axis component. As shown in Figure 2, the drone is located in the center of the ellipsoid, and the magnitude and direction of its velocity are represented by the vector, the positions of the obstacles are A and B, and the magnitude and direction of their velocities are represented by the vector and respectively. There exists a virtual ellipsoidal repulsive field around the UAV determined by the magnitude of its own motion velocity, and the obstacles located at the edges of the ellipsoidal potential field have different distances to the UAV. Therefore, the improved ellipsoidal repulsive field can give a larger repulsive influence to the obstacles located at high collision possibilities, and the improved repulsive force also decreases with increasing distance as the conventional repulsive force.

The simulation environment is set as a 100m100m100m area with six obstacles randomly distributed in the space, and the relevant parameters set in this paper are shown in Table 2 according to the discussion of the parameters of the artificial potential field method in the literature [7]. The simulation results are shown in Fig. (a) shows the traditional artificial potential field method, and Fig. (b) shows the improved artificial potential field method.