The repository is associated with our RAL + IROS 2022 submission Visibility-Aware Navigation with Batch Projection Augmented Cross-Entropy Method over a Learned Occlusion Cost.

• JAX
• bebop_simulator
• ACADO (If you want to run Nägeli et al implementation)
• odom_visualizer (If you need the RViz visualization)

After installing the dependencies, you can build our propsed MPC package as follows:

cd your_catkin_ws/src
git clone https://github.com/Houman-HM/cem_batch_projection_vis_planner
cd .. && catkin build
source your_catkin_ws/devel/setup.bash

In order to run the MPC for tracking a target in a world with 6 obstacles, follow the procedure below:

roslaunch cem_vis_planner 6_wall_world.launch

This launches a Gazebo environment with 6 walls spawned.

rosrun cem_vis_planner main_base_line.py

rosrun cem_vis_planner main_projection.py

There are two bag files available for moving the target with speeds of 0.5 and 1 m/s in the 6_wall environment. You can play either of them to test the algorithms.

roscd cem_vis_planner && cd target_trajectory_bag_files

rosbag play 6_wall_target_vel_05.bag // or

rosbag play 6_wall_target_vel_1.bag

You can also start teleoperating the target by publishing velocities on /target/cmd_vel topic. The drone should start following it as you are teleoperating the target.

Algorithm 1:

Algorithm 2:

• Edit global variables in test.c: maker position, obstacles initial position and velocity, quadrotor's initial position, weights
• Run make clean all only for the first time otherwise just make followed by ./test.
• To visualize, run python quad_plot.py

Look into code_gen.cpp file for acado settings, cost terms, constraints, etc.