Abstract
Multi-robot task allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to allocate a set of tasks to a set of robots so that the tasks can be completed by the robots while ensuring that a certain metric, such as the time required to complete all tasks, or the distance traveled, or the energy expended by the robots is reduced. We consider a scenario where tasks can appear dynamically and a task needs to be performed by multiple robots to be completed. We propose a new algorithm called SQ-MRTA (Spatial Queueing-MRTA) that uses a spatial queue-based model to allocate tasks between robots in a distributed manner. We have implemented the SQ-MRTA algorithm on accurately simulated models of Corobot robots within the Webots simulator for different numbers of robots and tasks and compared its performance with other state-of-the-art MRTA algorithms. Our results show that the SQ-MRTA algorithm is able to scale up with the number of tasks and robots in the environment, and it either outperforms or performs comparably with respect to other distributed MRTA algorithms.
Highlights
We measured the performance of the SQ-Multi-robot task allocation (MRTA) algorithm along the following metrics: the time required to complete all tasks while varying both the number of robots and tasks separately, the distance traveled by robots to perform tasks and the total idle time during which robots were waiting for other robots to perform tasks, so that tasks become available
A similar, linear scalability trend is observed for the completion times of all tasks as the number of tasks increases, with a different number of robots. These results indicate that the SQ-MRTA algorithm has linear scalability in the number of robots and tasks: when the number of tasks or robots increases, the algorithm performs the additional computation within a time that is linearly proportional to the number of tasks or robots added, and each additional robot does not impose any extra overhead on the computation done by the remaining robots
We compared its performance with three different state-of-the-art algorithms for MRTA, namely a decentralized greedy algorithm, a repeated auction algorithm and an optimized offline schedule based on the Hungarian algorithm
Summary
Multi-robot task allocation (MRTA) is an important aspect of multi-robot systems, where robots have to autonomously perform tasks that are distributed spatially and temporally within an environment.MRTA is used in numerous applications of robotic systems, including reconnaissance [1], unmanned search and rescue operations [2,3], cooperative transportation [4,5,6] and autonomous exploration [7,8].The fundamental problem addressed in MRTA is the following: Given a set of robots and a set of tasks that need to be performed by the robots, what is a suitable assignment of robots to tasks so that a global objective, such as the time to complete the tasks, or the distance traveled, or the energy expended by the robots is reduced. Multi-robot task allocation (MRTA) is an important aspect of multi-robot systems, where robots have to autonomously perform tasks that are distributed spatially and temporally within an environment. Many MRTA algorithms usually consider that only one robot is required to complete a task, that the information about the task is available a priori and that the task information does not change over time as robots operate in the environment. Static allocation of tasks to robots might not be valid in scenarios where multiple robots are required to complete a task, as the availability of a task to a robot can change dynamically as other robots perform operations on the task. To handle MRTA efficiently in such scenarios, it makes sense to investigate techniques that will allow robots to select tasks while considering their individual preferences, as well as the current task availabilities
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