## 3 Literature Review  
DTA for swarm robotics has gained much attention in the last few years. In [10], a self-organized task allocation method was proposed to partition a task among multiple robots in a swarm with limited information about the environment. The proposed method does not require explicit communication among the robots. That is, each robot determines its foraging strategy and which subtask to perform based on its local estimate of the global execution time, which is updated after each task is completed. This method was tested in an ARGoS simulator, and it was found that the robots' decision-making improved, which led to overall improvements in performance. However, even though pheromone-based memory can be advantageous in foraging, it performs task allocation under ideal conditions, i.e., when all tasks are equally important and the robots have equal capabilities. Moreover, there can be a tradeoff between the improvement in performance and the scalability of the method. [PERSON] et al. [19] also addressed the self-organized task allocation problem for swarms. They proposed a novel game-theoretical autonomous decision-making framework, which was tested in a dynamic environment. The main goal of their proposed framework was to achieve consensus among all agents regarding the best assignment for a current task. Even though the proposed framework was shown to perform well, it did not compared to existing methods. In [20], four different DTAs in swarm foraging were proposed. The first algorithm, Random-Choice, randomly selects the tasks and runs in a constant time. The second algorithm, Extreme-Comm, reduces the execution time but requires heavy communication among the robots. The third algorithm, Card-Dealer, sequentially assigns tasks to the robots to reduce communication but increases the execution time. The fourth algorithm, Tree-Recolor, combines Card-Dealer and Extreme-Comm to balance the execution time and communication. The algorithms were tested on a group of 25 iRobot SwarmBots. In the case of Random-Choice, communication was not required, but it had a high failure rate with small- to medium-sized swarms. Extreme-Comm was fast and accurate but required intensive communication that was heavily interrupted. Card-Dealer minimized the need for heavy communication, but the execution time was long because of its sequential task assignment. Finally, in the Tree-Recolor algorithm, problems arose due to communication hardware limitations. A two-step scheme for DTA was proposed by [PERSON] et al. [6] in swarm foraging. It first partitions the task and then autonomously allocates the subtasks to the robots. From the empirical test, it was found that the proposed task allocation approach outperformed the conventional evolutionary-based approach and was more effective. A DTA method was proposed by [PERSON] et al. [3] to perform self-organizing swarm foraging based on a response threshold model to allocate sequentially interdependent subtasks. The proposed model considered the demand of a given task in addition to the response threshold for the task; the demand decreases while the robot performs the task, and a robot with a higher response threshold is less likely to perform the task. This model regulates the proportion of robots with respect to the fraction of the task demand. Another threshold-based self-organized DTA method for swarm foraging was proposed in [21], to autonomously change the number of working robots. A sigmoid model was used to compute the response threshold, and then each robot could decide whether it should forage to reduce the traffic and congestion in a region. [PERSON] and [PERSON] [8] proposed a decentralized strategy to improve the response threshold model for DTA in swarm foraging. A task selection probability function was proposed for each robot to balance the task demands with respect to the working robots. Although the environment was unknown, each robot could use its local information in addition to the number of tasks assigned to its neighbors.