Abstract
Dynamic task allocation is a necessity in a group of robots. Each member should decide its own task such that it is most commensurate with its current state in the overall system. In this work, the response threshold model is applied to a dynamic foraging task. Each robot employs a task switching function based on the local task demand obtained from the surrounding environment, and no communication occurs between the robots. Each individual member has a constant-sized task demand history that reflects the global demand. In addition, it has response threshold values for all of the tasks and manages the task switching process depending on the stimuli of the task demands. The robot then determines the task to be executed to regulate the overall division of labor. This task selection induces a specialized tendency for performing a specific task and regulates the division of labor. In particular, maintaining a history of the task demands is very effective for the dynamic foraging task. Various experiments are performed using a simulation with multiple robots, and the results show that the proposed algorithm is more effective as compared to the conventional model.
Highlights
Multi-agent systems can be used to perform dynamic tasks
A response threshold model is applied for improving the performance of a foraging task
We suggest a method that includes the use of a puck history to estimate the global task demand
Summary
Multi-agent systems can be used to perform dynamic tasks. Each member of a group of robots should determine the current task that is most commensurate with its current surrounding labor states. There are two issues involved in performing tasks in a multi-robot system: the cooperation of robots and the division of labor among robots in a group. The cooperation involves performing a complex task as a group of robots instead of improving the ability of a single robot, while the division of labor involves efficiently managing tasks that are costly and time intensive. One possible approach for solving these types of problems is to establish a dynamic task allocation mechanism using adaptive processing that independently adapts all robots in a group to a dynamically-changing environment. The basic requirements for achieving this performance are maintaining the specialized individuals and processing tasks in parallel
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