Task Allocation for Multi-Agent Specialized Systems Using Probabilistic Estimate of Robots Competencies

An innovative task allocation scheme for a text multi-robotic system in a specific context is introduced in this paper, where functionalities of the individual robots are considered, and a probabilistic estimate of each robot specialization is computed. The problem is formulated based on the assumption that each robotic agent is qualified for performing specialized functionalities, and the expected tasks distributed in the surrounding environment enforce specific requirements. The task allocation algorithm evolves through three stages to compute individual robot allocation probabilities. First, recognizing the features of the target task is addressed by leveraging the output of a vision system in the sensing layer to drive the proposed text agent-task allocation scheme. Second, a matching strategy is formulated to match each robot’s unique functionalities with the corresponding features of target tasks. The specialization of each agent is developed in two approaches as a main part of the matching process: first, a binary association of the capabilities of each agent, and second, based on the suitability of each agent to tackle the various tasks. Finally, the developed robot-task-matching system is expanded to fully utilize the potential of the robot specializations, considering the agents attendance level with the availability of services of each agent. The developed framework is extensively validated through MATLAB simulations. To demonstrate the feasibility of the proposed system for real-life applications, the developed framework is implemented on real robots. The results show that the performance of the proposed allocation scheme is increased significantly when the suitability levels of the agents’ specializations inform the task allocation process and agent attendance levels are activated.