Task allocation for multi-robot teams in dynamic environments

2017 ◽  
pp. 483-492
Author(s):  
Maciej Hojda
Keyword(s):  
Task Allocation ◽  
Dynamic Environments ◽  
Robot Teams ◽  
Multi Robot

scholarly journals Auction-Based Task Allocation for Multi-robot Teams in Dynamic Environments

2015 ◽  
pp. 246-257 ◽  
Author(s):  
Eric Schneider ◽  
Elizabeth I. Sklar ◽  
Simon Parsons ◽  
A. Tuna Özgelen
Keyword(s):  
Task Allocation ◽  
Dynamic Environments ◽  
Robot Teams ◽  
Multi Robot

A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments

2021 ◽  
Author(s):  
Ching-Wei Chuang ◽  
Harry H. Cheng
Keyword(s):  
Bayesian Networks ◽  
Task Allocation ◽  
Low Cost ◽  
Dynamic Environments ◽  
Modern World ◽  
Robot System ◽  
Novel Approach ◽  
Robot Systems ◽  
Robot Task ◽  
Multi Robot

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.

scholarly journals Data-Driven Adaptive Task Allocation for Heterogeneous Multi-Robot Teams Using Robust Control Barrier Functions

2021 ◽  
Author(s):  
Yousef Emam ◽  
Gennaro Notomista ◽  
Paul Glotfelter ◽  
Magnus Egerstedt
Keyword(s):  
Robust Control ◽  
Task Allocation ◽  
Data Driven ◽  
Barrier Functions ◽  
Robot Teams ◽  
Multi Robot

scholarly journals Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities

2020 ◽  
Author(s):  
Yousef Emam ◽  
Siddharth Mayya ◽  
Gennaro Notomista ◽  
Addison Bohannon ◽  
Magnus Egerstedt
Keyword(s):  
Task Allocation ◽  
Robot Teams ◽  
Multi Robot

Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm

2018 ◽  
Vol 15 (2) ◽  
pp. 627-636 ◽  
Author(s):  
K. Padmanabhan Panchu ◽  
M. Rajmohan ◽  
M. R. Sumalatha ◽  
R. Baskaran
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Completion Time ◽  
Route Planning ◽  
Task Completion ◽  
Task Completion Time ◽  
Robot Teams ◽  
Reconfigurable Robot ◽  
Robot Tasks ◽  
Multi Robot

This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.

Sign in / Sign up

Export Citation Format

Share Document