## 3 Methodology
### Dynamic Task Allocation
Our C-CBAA can prevent this possibility of collision and instruct the _P3_ agent in the purple group to change its position to the left when its group approaches to the target. The selected path also avoids the collision within the group by routing from the back of the group. The same path planning strategy can be found from the case of target decreasing scenario as shown in Figure 8b. The _B1_ agent arrives at the center position of the line formation first. It will circle to the left position to avoid the collision with the next arriving agent to the center position. 4. Reach to target simultaneously and avoid obstacles: The C-CBAA considers the constraints of reaching the target at the same time and avoids obstacles in task allocation during the flight. Figure 8a shows the trajectories of agents before removing

Figure 9: Compare the performance of two algorithms. (**a**) Increasing targets. (**b**) Decreasing targets. the red target during the movement. The \(R2\) agent is closer to the yellow target and easier to run around the obstacle than the \(R1\) agent. Therefore, it is assigned to the yellow target after removing the red target. Another example can be seen from Figure 6b. The \(R2\) agent changes its position from the back to the left to avoid the possibility of collision to the obstacle. The same can be found in the red group of Figure 7b. ## 6 Conclusions

In this research, we tackled scenarios of multi-targets task allocation with random obstacles in real time movement environment. Two algorithms are proposed under the distributed MAS architecture to achieve the target autonomously. The Committee and Consensus Base Auction Algorithm and Committee-Based Consensus Dynamic Trajectory Planning Algorithm can perform task re-allocation and avoid obstacles while maintaining a compact formation in real time. Simulation results confirmed that the proposed algorithms C-CBAA were able to converge faster than the baseline CBAA methods under various scenarios. The current task allocation is based on a load balancing method by assuming each agent has the same load during the operation and each agent only assigns one task. In the future, we are considering allowing agents to take more than one task within the time limits. In reality, task allocation should also consider the priority of the task, task requirements and the functional capabilities of each agent. The same for the path planning during the obstacle avoiding process. There should be a weighting associated with the risk for a path option in obstacles avoidance. We plan to allow different weightings to targets as well as obstacles for path planning in the future. We also plan to expand our algorithms to a 3D application such as the operation of UAVs. Conceptualization, W.-Y.Y., V.-W.S. and Y.-L.L. ; methodology, V.-W.S. ; software, X.-Q.H. and H.-Y.L. ; validation, X.-Q.H. and H.-Y.L. ; formal analysis, Y.-L.L. ; investigation, W.-Y.Y., X.-Q.H. and H.-Y.L. ; resources, W.-Y.Y. ; data curation, W.-Y.Y. ; writing--original draft preparation, W.-Y.Y. ; writing--review and editing, W.-Y.Y. and V.-W.S. ; visualization, Y.-L.L. ; supervision, V.-W.S. and Y.-L.L. ; project administration, W.-Y.Y. and V.-W.S. ; funding acquisition, W.-Y.Y. and Y.-L.L. All authors have read and agreed to the published version of the manuscript. This work was financially supported by National Chung-Shan Institute of Science and Technology, R.O.C (XC08347 PE18 PE-CS, developing of the algorithm) and Ministry of Science and Technology, R.O.C (MOST 109-2623-E-606-003-D, approach and scenario application). Not applicable. The authors declare no conflict of interest. ## References

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