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caric_baseline

caric_baseline

This repo is for CDC2023 challange caric_baseline. The challenge details can be find in https://ntu-aris.github.io/caric/ The main task can be divided into two part, the Task Assignment Part and the Swarm Planning Part. Until 2023/08/23 the baseline is just for mbs task, we wish it can help the participants to solve some techinical problems.

Update Log

2023/08/23 Update the mbs baseline

Quick Demo

Pull down this repo. Catin build and launch the demo_paths.launch in this repo.

Task Assignment Part

Notice: The code here is not a general method for all subtasks. Until 2023/08/13 the code is only for mbs tasks.

Methodology

The task assignment means " who will search which bounding box" and it can also be simplified as a Multiple Traveling Salesman Problem (MTSP). In the baseline, we use an exhaustive search to solve this problem. One drawback of this approach is that it incurs significant time costs when dealing with a large number of bounding boxes, as it requires extensive efforts to seek solutions for the problem. In certain scenarios, such as the crane scene, obtaining a solution can take as long as 25 minutes. Therefore, participants are encouraged to further develop the algorithm.

Code Example

initial_task.h/cpp

Swarm Planning Part

Methodology

In the baseline approach, due to the mismatch between the capabilities of the explorer and the photographer, a grouping strategy is employed during task initialization based on the number of explorers. The positioning control of the target bounding box follows a strategy of exploring along the longest edge before scanning. Greedy strategies are utilized for controlling the gimbal orientation and the orientation of the agent. Upon completion of the task, a return to the starting point is executed.

Code Example

Regarding technical specifics, we autonomously constructed a three-dimensional grid map and established sub-maps for each bounding box to facilitate task management. A concrete illustration is provided as follows:
baseline_planner.h/cpp

Reference

If you are using this package, please cite our paper as follows

@inproceedings{xu2024costeffective,
  title={A Cost-Effective Cooperative Exploration and Inspection Strategy for Heterogeneous Aerial System},
  author={Xu, Xinhang and Cao, Muqing and Yuan, Shenghai and Nguyen, Thien Hoang and Nguyen, Thien-Minh and Xie, Lihua},
  booktitle={2024 IEEE 18th International Conference on Control & Automation (ICCA)},
  pages={673-678},
  year={2024},
  organization={IEEE}
}