# Multirobot Task Planning Method Based on the Energy Penalty Strategy
## 6 Conclusions  
To address the problem of multirobot task planning, an MRTA method based on the energy penalty strategy is proposed, which targets the twofold objective of minimizing energy consumption and increasing energy balance acting on task number and task sequence. The  
Figure 17: Path planning diagram of Robot 2 after task sequence optimization from Experiment 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task Optimization** & \(\mathbf{TS(R_{2})}\) & \(\mathbf{L(R_{2})(m)}\) & \(\mathbf{E(R_{2})(J)}\) \\ \hline Before & \{(\)(1),2,25,15,20,22,19,13,5,18,28,4,27\(\}\)102 & 343.05 \\ After & \{(\)(1),4,27,2,15,25,18,28,5,13,19,22,20\(\}\)83 & 283.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The optimal task sequence and operation parameters of Robot 2 from Experiment 5.  
first stage of the algorithm calculates the energy consumption of the robot with the shortest linear distance between adjacent task nodes in the barrier-free task environment. Then, it reconstructs the energy penalty objective function with the total energy consumption and penalty energy, after which it iteratively calculates the optimal scheme of multitask allocation through genetic operators to achieve the dual goals of total energy consumption optimization and energy balance. The results from experiment 1, 2, and 3 highlight a significant improvement in energy consumption and energy balance compared with the basic GA and related literature algorithms. The second stage of the algorithm is to form a complex task scenario by adding rules and irregular obstacles in the task environment of the previous stage. The distance between adjacent task nodes and their energy consumption will change due to the path obstacle avoidance requirements of the complex scenario. Thus, it is necessary to recalculate and optimize the optimal task allocation scheme of the first stage. The A* algorithm is then used to obtain the optimal path for obstacle avoidance and calculate the energy consumption. On the premise of not changing the number of robot tasks, the GA is used again to optimize the robot task sequence with excessive energy consumption. Experiments 4 and 5 were conducted in two different scenarios (regular and irregular obstacle distribution, respectively), and the results indicate the effectiveness of the algorithm. Furthermore, the energy penalty strategy plays a driving role in iterative optimization in task planning. The strategy can also be combined with other optimization algorithms for implementation verification in different application environments. The algorithm proposed in this paper is implemented in stages according to the change in the task environment, especially in the second stage, where the task sequence is re-optimized without changing the number of tasks; therefore, it is relatively simple and feasible and can be used to solve large-scale optimization problems. Furthermore, the algorithm model can also adjust the size of the punishment by setting the penalty factor and its change to further study the rationality of the punishment strategy. Given that the second stage of the algorithm is only secondary optimization for the task sequence of the robot with excessive energy consumption, the search space can be extended to all robots. This is also the direction of forthcoming research. Conceptualization, L.L. and L.Z. ; methodology, L.L. and W.J. ; software, L.Z. ; validation, L.L., L.L. and W.J. ; formal analysis, L.L. and L.Z. ; investigation, X.C. ; resources, L.L. ; data curation, L.L. and L.Z. ; writing--original draft preparation, L.L. and L.Z. ; writing--review and editing, L.L. and L.Z. ; visualization, L.L. and L.Z. ; supervision, X.C. ; project administration, L.L. ; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Key Project of Natural Science of Anhui Provincial Department of Education (Project ID. KJ2018A0102, 2022 AH050992), and the Anhui Institute of Future Technology enterprise cooperation project (Project ID.