A Policy Synthesis-Based Framework for Robot Rescue Decision-Making in Multi-Robot Exploration of Disaster Sites

2018 ◽  
Author(s):  
Sarah Al-Hussaini ◽  
Jason M. Gregory ◽  
Satyandra K. Gupta
Keyword(s):  
Decision Making ◽  
Multi Robot

Design and Development of Multi-Robot Architecture and Decision-Making Model

2007 ◽  
Author(s):  
Shu-Qin Li ◽  
Xiao-Hua Yuan ◽  
Zhen-Min Tang ◽  
Lu Yang ◽  
Jing-Yu Yang
Keyword(s):  
Decision Making ◽  
Design And Development ◽  
Robot Architecture ◽  
Decision Making Model ◽  
Multi Robot

scholarly journals A Framework for Information Distribution, Task Execution and Decision Making in Multi-Robot Systems

2010 ◽  
Vol E93-D (6) ◽  
pp. 1352-1360 ◽  
Author(s):  
Matthias RAMBOW ◽  
Florian ROHRMÜLLER ◽  
Omiros KOURAKOS ◽  
Drazen BRŠCIC ◽  
Dirk WOLLHERR ◽  
...  
Keyword(s):  
Decision Making ◽  
Information Distribution ◽  
Task Execution ◽  
Robot Systems ◽  
Multi Robot

scholarly journals Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 484
Author(s):  
Alberto Viseras ◽  
Zhe Xu ◽  
Luis Merino
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Physical Process ◽  
Wind Field ◽  
Complex Dynamics ◽  
Information Gathering ◽  
Distributed Decision Making ◽  
Novel Approach ◽  
Distributed Decision ◽  
Multi Robot

Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-robot cooperation have been proposed in the literature. Most of these algorithms employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they cannot deal with inter-robot restrictions such as collision avoidance or communication constraints. This paper presents a novel approach for multi-robot information gathering (MR-IG) that tackles the two aforementioned restrictions: (i) discretization of robot’s state space, and (ii) dealing with inter-robot constraints. Here we propose an algorithm that employs: (i) an underlying model of the physical process of interest, (ii) sampling-based planners to plan paths in a continuous domain, and (iii) a distributed decision-making algorithm to enable multi-robot coordination. In particular, we use the max-sum algorithm for distributed decision-making by defining an information-theoretic utility function. This function maximizes IG, while fulfilling inter-robot communication and collision avoidance constraints. We validate our proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness and scalability with respect to the number of robots of our approach.

Multi-robot decision making based on coordination graphs

2009 ◽  
Author(s):  
Jin Wang ◽  
Tong Wang ◽  
Xiao Wang ◽  
Xiangping Meng
Keyword(s):  
Decision Making ◽  
Multi Robot

scholarly journals Human Decision-Making Behavior Modeling for Human Multi-Robot Interaction System

2020 ◽  
Vol 53 (2) ◽  
pp. 10202-10207
Author(s):  
Wenhua Wu ◽  
Jie Huang ◽  
Zhenyi Zhang
Keyword(s):  
Decision Making ◽  
Behavior Modeling ◽  
Robot Interaction ◽  
Interaction System ◽  
Human Decision ◽  
Decision Making Behavior ◽  
Multi Robot

scholarly journals Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 341 ◽  
Author(s):  
Hu ◽  
Xu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Action Function ◽  
State Action ◽  
Markov Decision ◽  
Decision Making System ◽  
Multi Agent ◽  
Function Approximator ◽  
Multi Robot

Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.

Fuzzy logic decision making for multi-robot security systems

2010 ◽  
Vol 34 (2) ◽  
pp. 177-194 ◽  
Author(s):  
Malrey Lee ◽  
Mahmoud Tarokh ◽  
Matthew Cross
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Security Systems ◽  
Multi Robot
Sign in / Sign up

Export Citation Format

Share Document