Improvement and Simulation of Contract-Net-Based Task Allocation for Multi-robot System

2012 ◽  
pp. 61-67
Author(s):  
Hao Lili ◽  
Yang Huizhen
Keyword(s):  
Task Allocation ◽  
Robot System ◽  
Contract Net ◽  
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 DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)

2021 ◽  
Vol 3 (1) ◽  
pp. 69-98
Author(s):  
Paul Gautier ◽  
Johann Laurent
Keyword(s):  
Reinforcement Learning ◽  
Task Allocation ◽  
Computing System ◽  
Processing Load ◽  
Robot System ◽  
Research Challenges ◽  
Local Solutions ◽  
Overall Efficiency ◽  
Robot Task ◽  
Multi Robot

Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.

scholarly journals Dynamic Task Allocation in Multi-Robot System Based on a Team Competition Model

2021 ◽  
Vol 15 ◽  
Author(s):  
Kai Jin ◽  
Pingzhong Tang ◽  
Shiteng Chen ◽  
Jianqing Peng
Keyword(s):  
Task Allocation ◽  
Real Life ◽  
Interesting Case ◽  
Competition Model ◽  
Allocation Problem ◽  
Equilibrium Strategy ◽  
Robot System ◽  
Dynamic Task ◽  
Perfect Equilibria ◽  
Multi Robot

In recent years, it is a trend to integrate the ideas in game theory into the research of multi-robot system. In this paper, a team-competition model is proposed to solve a dynamic multi-robot task allocation problem. The allocation problem asks how to assign tasks to robots such that the most suitable robot is selected to execute the most appropriate task, which arises in many real-life applications. To be specific, we study multi-round team competitions between two teams, where each team selects one of its players simultaneously in each round and each player can play at most once, which defines an extensive-form game with perfect recall. We also study a common variant where one team always selects its player before the other team in each round. Regarding the robots as the players in the first team and the tasks as the players in the second team, the sub-game perfect strategy of the first team computed via solving the team competition gives us a solution for allocating the tasks to the robots—it specifies how to select the robot (according to some probability distribution if the two teams move simultaneously) to execute the upcoming task in each round, based on the results of the matches in the previous rounds. Throughout this paper, many properties of the sub-game perfect equilibria of the team competition game are proved. We first show that uniformly random strategy is a sub-game perfect equilibrium strategy for both teams when there are no redundant players. Secondly, a team can safely abandon its weak players if it has redundant players and the strength of players is transitive. We then focus on the more interesting case where there are redundant players and the strength of players is not transitive. In this case, we obtain several counterintuitive results. For example, a player might help improve the payoff of its team, even if it is dominated by the entire other team. We also study the extent to which the dominated players can increase the payoff. Very similar results hold for the aforementioned variant where the two teams take actions in turn.

Evolutionary Robotics Approach to Autonomous Task Allocation for a Multi-Robot System

2010 ◽  
pp. 121-128 ◽  
Author(s):  
Kazuhiro Ohkura ◽  
Toshiyuki Yasuda ◽  
Yoshiyuki Matsumura ◽  
Tomoya Matsuda
Keyword(s):  
Task Allocation ◽  
Evolutionary Robotics ◽  
Robot System ◽  
Multi Robot

Periodicity Constraint Task Allocation Model Based on Reputation for the Multi-Robot System

2012 ◽  
Vol 11 (1) ◽  
pp. 473-478
Author(s):  
Zhiguo Shi ◽  
Jun Tu ◽  
Junming Wei ◽  
Zhiliang Wang ◽  
Qiao Zhang
Keyword(s):  
Task Allocation ◽  
Robot System ◽  
Allocation Model ◽  
Model Based ◽  
Multi Robot

A Distributed Task Allocation Algorithm for a Multi-Robot System in Healthcare Facilities

2014 ◽  
Vol 80 (1) ◽  
pp. 33-58 ◽  
Author(s):  
Gautham P. Das ◽  
Thomas M. McGinnity ◽  
Sonya A. Coleman ◽  
Laxmidhar Behera
Keyword(s):  
Task Allocation ◽  
Robot System ◽  
Healthcare Facilities ◽  
Allocation Algorithm ◽  
Distributed Task Allocation ◽  
Multi Robot
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