Self-organized Task Partitioning in a Swarm of Robots

Abstract

Abstract. In this work, we propose a method for self-organized adaptive task partitioning in a swarm of robots. Task partitioning refers to the decomposition of a task into less complex subtasks, which can then be tackled separately. Task partitioning can be observed in many species of social animals, where it provides several benefits for the group. Selforganized task partitioning in artificial swarm systems is currently not widely studied, although it has clear advantages in large groups. We propose a fully decentralized adaptive method that allows a swarm of robots to autonomously decide whether to partition a task into two sequential subtasks or not. The method is tested on a simulated foraging problem. We study the method's performance in two different environments. In one environment the performance of the system is optimal when the foraging task is partitioned, in the other case when it is not. We show that by employing the method proposed in this paper, a swarm of autonomous robots can reach optimal performance in both environments.

Utilizing Task Partitioning for Self-Organized Allocation of Partially Sequential Tasks

“…From matrix D, the dependency pairs are (1, 3), (3,4), (4,6), (5, 7), (6, 5), (7,8), (10,9), (9,2). These pairs are combined, and we end up with two task sequences that must be followed: 3,4,6,5,7,8] s2 = [10,9,2].…”

“…A natural task partitioning is created where one group of ants specializes in cutting the leaves (see fig. 1), and another group in transporting the fallen leaves back to the nest [4]. The sequential dependency comes from the fact that the leaves must be cut before transporting.…”

A Distributed Algorithm for Un-balanced Partitioning of a Swarm of Autonomous Mobile Robots and Its Performance Analysis

“…These concepts of swarm robotics have various applications such as task allocation [6], military operations, search and rescue victims, lawn mowing and sweeping, space mission, operations like enclosing an invader [8], area exploration and coverage [7] etc. Existing task allocation techniques [6,9,10] partition the swarm into several groups and dynamically allocate each group of robots to multiple tasks. They may use balanced partitioning techniques [1][2][3] that partitions n number of robots in k-size balanced groups.…”

Distributed Sequential Task Allocation in Foraging Swarms

“…At an intermediate location robots who have finished the first sub-task transfer the resource to robots waiting to start the second. In some studies, this transfer is directly between robots [10], [12], [14], whereas others make use of a cache in which resources can be deposited after completion of the first sub-task, regardless whether a robot is ready to start the second [9], [13], [15]. These studies, however, all use a single source and nest, and hence resources have a single entry-, exchange-and exit-point.…”

“…Current studies of sequential task allocation either focus on ensuring optimal usage of resources (e.g. robots, time) on each sub-task globally [10]- [12], or on optimal switching between sequential and non-sequential processing of tasks [9], [13] (when coordination for partitioning tasks incurs an overhead). However, a problem not addressed so far is distributed sequential task allocation: the optimal use of fractions of resources locally in a distributed problem.…”