Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

Amanda Whitbrook,Gerald Schaefer,Joanna Turner,Andrea Soltoggio,Qinggang Meng

doi:10.1109/tcyb.2017.2743164

Amanda Whitbrook, Gerald Schaefer + Show 3 more

Open Access

https://doi.org/10.1109/tcyb.2017.2743164

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Abstract

This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

Highlights

M ULTIROBOT systems are increasingly employed to complete jobs and missions in various fields including search and rescue [1]–[4], space and underwater exploration [5], support in healthcare facilities [6], surveillance and target tracking [7], [8], product manufacturing [9], [10], pick-up and delivery, and logistics
consensus-based bundle algorithm (CBBA) is an established benchmark for comparison in distributed task allocation problems and provides a useful metric for general comparisons with similar algorithms
Preliminary experiments revealed that changing other parameter settings such as the starting positions of the vehicles, e.g., all vehicles starting from the same position, did not significantly affect the number of task allocations

Summary

Introduction

M ULTIROBOT systems are increasingly employed to complete jobs and missions in various fields including search and rescue [1]–[4], space and underwater exploration [5], support in healthcare facilities [6], surveillance and target tracking [7], [8], product manufacturing [9], [10], pick-up and delivery, and logistics. A team of homogeneous or heterogeneous specialized robots can cover more ground and be more resilient to failures than a single all-purpose robot [11], [12]. Manuscript received June 28, 2016; revised April 6, 2017 and July 28, 2017; accepted August 7, 2017. Date of publication September 28, 2017; date of current version August 16, 2018.

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