Multiple Robots Task Allocation for Cleaning and Delivery

2016 ◽  
pp. 195-214 ◽  
Author(s):  
Seohyun Jeon ◽  
Minsu Jang ◽  
Daeha Lee ◽  
Young-Jo Cho ◽  
Jaehong Kim ◽  
...  
Keyword(s):  
Task Allocation ◽  
Multiple Robots

Game theory-based negotiation for multiple robots task allocation

Robotica ◽  
2013 ◽  
Vol 31 (6) ◽  
pp. 923-934 ◽  
Author(s):  
Rongxin Cui ◽  
Ji Guo ◽  
Bo Gao
Keyword(s):  
Game Theory ◽  
Task Allocation ◽  
Optimal Solution ◽  
Multiple Robots ◽  
Negotiation Strategy ◽  
Contract Net ◽  
Initial Task ◽  
The Game Theory ◽  
Task Reallocation ◽  
Negotiation Mechanism

SUMMARYThis paper investigates task allocation for multiple robots by applying the game theory-based negotiation approach. Based on the initial task allocation using a contract net-based approach, a new method to select the negotiation robots and construct the negotiation set is proposed by employing the utility functions. A negotiation mechanism suitable for the decentralized task allocation is also presented. Then, a game theory-based negotiation strategy is proposed to achieve the Pareto-optimal solution for the task reallocation. Extensive simulation results are provided to show that the task allocation solutions after the negotiation are better than the initial contract net-based allocation. In addition, experimental results are further presented to show the effectiveness of the approach presented.

scholarly journals Cooperative Multi-Robot Task Allocation with Reinforcement Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 272
Author(s):  
Bumjin Park ◽  
Cheongwoong Kang ◽  
Jaesik Choi
Keyword(s):  
Reinforcement Learning ◽  
Task Allocation ◽  
Decision Process ◽  
Large Scale ◽  
Heuristic Methods ◽  
Learning Method ◽  
Multiple Robots ◽  
Markov Decision ◽  
Robot Task ◽  
Multi Robot

This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.

Development of a reduced human user input task allocation method for multiple robots

2012 ◽  
Vol 60 (10) ◽  
pp. 1231-1244 ◽  
Author(s):  
Praneel Chand ◽  
Dale A. Carnegie
Keyword(s):  
Task Allocation ◽  
Multiple Robots ◽  
User Input ◽  
Allocation Method

Multiple robots task allocation for cleaning a large public space

2015 ◽  
Author(s):  
Seohyun Jeon ◽  
Minsu Jang ◽  
Daeha Lee ◽  
Young-Jo Cho ◽  
Jaeyeon Lee
Keyword(s):  
Public Space ◽  
Task Allocation ◽  
Multiple Robots

scholarly journals Heuristic based Task Allocation Algorithm for Multiple Robots Using Agents

2013 ◽  
Vol 64 ◽  
pp. 844-853 ◽  
Author(s):  
Thareswari Nagarajan ◽  
Asokan Thondiyath
Keyword(s):  
Task Allocation ◽  
Multiple Robots ◽  
Allocation Algorithm
Sign in / Sign up

Export Citation Format

Share Document