Distributed Multi-vehicle Task Assignment and Motion Planning in Dense Environments

Python Implementation of multi-vehicle task assignment and motion planning (MVTAMP), including task assignment algorithms: consensus-based bundle algorithm ( CBBA), lazy sample-based task allocation (LSTA), decentralized sample-based task allocation (DSTA), and our lazy-based review consensus algorithm (LRCA); motion planning algorithms: optimal reciprocal collision avoidance (ORCA), D*, and our guidance point strategy (GOS). Finally, a novel hierarchical method, LRGO, for solving the MVTAMP applied in non-holonomic vehicles is presented in this repository.

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Description

We present an novel method for solving the multi-vehicle task assignment and motion planning (MVTAMP) problem for non-holonomic vehicles, in which a fleet of non-holonomic vehicles is assigned to visit a series of targets to clear them efficiently, and each vehicle needs to travel to a specific ending area once it finished all missions in the shortest possible time.

About

Paper: Distributed Multi-vehicle Task Assignment and Motion Planning in Dense Environments, Gang Xu, Xiao Kang, Helei Yang, Yuchen Wu, Junjie Cao, Weiwei Liu, and Yong Liu, in IEEE Transactions on Automation Science and Engineering (T-ASE) 2023

Vedio: LRGO-Demo

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Requirement

pip install pandas
pip install random
pip install heapq
pip install scipy
pip install json
pip install collections
pip install matplotlib

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Applications

Phase 1:
cd run_example
python run_exam_lrgo.py   
In the file run_example, you can select the pyhton scripts to test different scenarios. 

Phase 2: Visualization
cd draw
python annimate.py

Results

• The first simulation: 50 vehicles visit 203 targets for clearing them and move to the start area.

• The results of evaluation.

• The first real-world experiment: 5 vehicle visit 11 targets.

• The first real-world experiment: 5 vehicle visit 11 targets and move to the start area.

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Citation

@article{xu2023distributed,
  title={Distributed Multi-Vehicle Task Assignment and Motion Planning in Dense Environments},
  author={Xu, Gang and Kang, Xiao and Yang, Helei and Wu, Yuchen and Liu, Weiwei and Cao, Junjie and Liu, Yong},
  journal={IEEE Transactions on Automation Science and Engineering},
  year={2023},
  publisher={IEEE}
}