Task-Allocation-of-mutiple-UAVs-for-Aerial-robot-Construction
This project is to implement the task allocation alogrithms of multi-robot task allocation (MRTA) problems.
Based on the paper of CHEN Xia and QIAO Yan-zhi in Summary of unmanned aerial vehicle task allocation [1], the algorithms can be generally divided into 4 parts:
1. Centralized task allocation methods
Centralized control system indicates that the group of UAVs (Unmanned Aerial Vehicles) are regulated and controlled by a single control center, including the signal transmission, communication between UAVs and the control commands of UAVs. The typical models are MTSP, VRP, MILP, DNFO, CMTAP.
1.1 Optimization methods
a. Exhaustive method
b. Mixed-Integer Linear Programming (MIP)
c. Constraint Programming (CP)
d. Graph-based method
1.2 Heuristic algorithm
a. List Scheduling (LS)
The common LS methods:
Dynamic List Scheduling (DLS), Multi-Dimensional Dynamic List Scheduling (MDLS), Multi-Priority List Dynamic Scheduling (MPLDS) and so on.
b. Intelligent optimization algorithm
I. Evolutionary algorithms (EA)
Genetic Algorithm (GA), Evolutionary Programming (GP), Evolution Strategy (ES) and Evolutionary Programming (EP)
II. Swarm Intelligence Algorithm (SIA)
Particle Swarm Optimization (PSO) and Ant Colony Optimization(ACO)
III. Others
Artificial Immune (AI), Tabu Search (TS) and Simulated Annealing Algorithm (SA)
2. Distributed task allocation methods
Distributed control system is different from Centralized control system in the way of signal transmission. The Distributed control system can allow UAVs communicated with each other in the group.
2.1 ContractNet
2.2 Market-Based Approaches [2]
first price auctions, Dynamic role assignment, Trade robots, Murdoch, Demircf, M+, etc.
3. Hierarchical distributed task allocation methods
This method is basically combine the first 2 methods. There is a centralized control system to control several UAV groups and for each group there is a distributed control system. The structure is shown below.
4. Further development methods
4.1 Task allocation under uncertain circumstances
4.2 Task allocation for multiple UAVs with different types
4.3 Dynamic real-time task assignment
4.4 Static Game Theory
4.5 Dynamic Game Theory
Finally, the Ant Colony Optimization (ACO) and Hungarian Algorithm have been implemented.
The simulated initial map in 2D and 3D are displayed, and the detailed results are indicated in subfloders.
Referenceļ¼
[1] Chen, Xia, and Yan-zhi Qiao. "Summary of unmanned aerial vehicle task allocation." Journal of Shenyang Aerospace University 33.6 (2016): 1-7.
[2] Wang, Jianping, Yuesheng Gu, and Xiaomin Li. "Multi-robot task allocation based on ant colony algorithm." Journal of Computers 7.9 (2012): 2160-2167.