## 9 Conclusions  
In this work, we proposed the task quality as a new factor to be considered in the dynamic multi-robot task allocation (MRTA) problem to indicate the quality of executing a specific task. We also considered workload balancing and traveled distance in the assignment process. The MRTA problem was formulated as an auction process and behavioral process. DYMO-Auction approach was proposed as a distributed multi-objective task allocation approach for dynamic task allocation. We targeted the scenarios where tasks with a quality requirement appear dynamically during the mission. We assumed no prior information about the task and available robots, in particular, tasks location, quality requirements, and the current positions and loads on each robot. We aimed to achieve three objectives: maximize the quality satisfaction, balance the load among available robots, and minimize the total traveled distance. We addressed the MRTA problem as an auction process.  
The DYMO-Auction approach combines these objectives using the weighted sum model (WSM) to compute the cost function. The weights were selected and generalized to scale. The proposed approach was tested against the sequential single-item approach (SSI) method. The SSI auction is presented with global information about the tasks and robots, whereas in DYMO-Auction, a task appears with its information dynamically over time. The proposed DYMO-Auction approach obtained comparable results to the SSI method in terms of quality satisfaction, load balancing, and traveled distance, given the limited information available compared to the SSI auction. We conducted testbed experiments for DYMO-Auction using real Turtlebot2 robots and performed the simulation under the same real experimental parameters; the results showed the same trend as the simulation results.  
In future studies, the proposed method can be extended to form a generalized framework that considers task waiting time, task priority, task's details changes over execution time. Auction approaches may suffer from communication overhead. In particular, if the number of robots  
Figure 19: Average total travelled distance per robot (Turtlebot2).  
Figure 18: Average number of tasks assigned to each Turtlebot2 robot, where the total number of tasks = 21.  
is large. Challenges may also arise if there are a small number of bidders in the auction. As a solution, hop count can be used to expand or narrow the pool of auction process participants.  
Conceptualization, U.B., M.A. and A.K.; data curation, M.A. and S.T.; formal analysis, U.B.; funding acquisition, U.B.; investigation, U.B., M.A. and A.K.; methodology, U.B., M.A., A.K. and S.T.; project administration, U.B.; resources, U.B.; software, M.A. and S.T.; supervision, U.B. and A.K.; validation, U.B., M.A., A.K. and S.T.; Visualization, M.A. and S.T.; writing--original draft, M.A.; writing--review and editing, U.B., A.K. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH)--King Abdulaziz City for Science and Technology through the Science and Technology Unit at King Fahd University of Petroleum and Minerals (KFUPM), the Kingdom of Saudi Arabia, award project No. 11-ELE2147-4. In addition, [PERSON] would like to acknowledge the support by the Robotics and Internet-of-Things Lab of Prince Sultan University.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.