Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments

About

This repository holds the implementation code for the simulation scenario with 2 robots vs. 3 non-adversarial targets that appears in the paper

Zirui Xu, Xiaofeng Lin, and Vasileios Tzoumas, "Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments," Robotics: Science and Systems, 2023.

Run the simulation

• run main.m

Design new scenarios

To change the number of robots/the number of targets/the type of a target, please modify the following parameters in main.m:

    num_robot     % number of robots
    num_tg        % number of targets
    type_tg       % type of targets ("normal" or "adversarial")

To modify settings of robots and targets, please change the following parameters in scenarios_settings.m (notice all variables should have matching dimensions):

    v_robot       % speed of robots
    r_senses      % sensing range of robots
    fovs          % field of view in degree
    v_tg          % speed of targets
    yaw_tg        % initial yaw angles of targets
    motion_tg     % type of motion of targets (circle, straight)
    x_true_init   % initial pose of robots
    tg_true_init  % initial pose of targets

Acknowledgement

We thank Nikolay Atanasov for sharing the code for "Decentralized active information acquisition: Theory and application to multi-robot SLAM".

License

The project is licensed under MIT License.

Citation

If you have an academic use, please cite:

@inproceedings{xu2023bandit,
  title={Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments},
  author={Xu, Zirui and Lin, Xiaofeng and Tzoumas, Vasileios},
  journal={Robotics: Science and Systems},
  year={2023}
}