A Hybrid Decentralized Coordinated Approach for Multi-Robot Exploration Task

2018 ◽  
Vol 62 (9) ◽  
pp. 1284-1300 ◽  
Author(s):  
Khalil Mohamed ◽  
Ayman El Shenawy ◽  
Hany Harb
Keyword(s):  
Task Assignment ◽  
Target Cells ◽  
Local Data ◽  
Networked Robots ◽  
Exploration Process ◽  
Robot Systems ◽  
Markov Decision ◽  
Partially Observable ◽  
Assignment Approach ◽  
Multi Robot

Abstract Exploring the environment using multi-robot systems is a fundamental process that most automated applications depend on. This paper presents a hybrid decentralized task assignment approach based on Partially Observable Semi-Markov Decision Processes called HDec-POSMDPs, which are general models for multi-robot coordination and exploration problems in which robots can make their own decisions according to its local data with limited communication between the robot team. In this paper, a variety of multi-robot exploration algorithms and their comparison have been tackled. These algorithms, which have been taken into consideration, are dependent on different parameters. Collectively, there are five metrics maximize the total exploration percentage, minimize overall mission time, reduce the number of hops in the networked robots, reduce the energy consumed by each robot and minimize the number of turns in the path from the start pose cells to the target cells. Therefore, a team of identical mobile robots is used to perform coordination and exploration process in an unknown cell-based environment. The performance of the task depends on the strategy of coordination among the robots involved in the team. Therefore, the proposed approach is implemented, tested and evaluated in MRESim computer simulator, and its performance is compared with different coordinated exploration strategies for different environments and different team sizes. The experimental results demonstrate a good performance of the proposed approach compared to the four existing approaches.

scholarly journals Applications of DEC-MDPs in Multi-Robot Systems

Robotics ◽  
2013 ◽  
pp. 143-165
Author(s):  
Aurélie Beynier ◽  
Abdel-Illah Mouaddib
Keyword(s):  
Decentralized Control ◽  
Decision Processes ◽  
Decision Problems ◽  
Large Set ◽  
Complex Environments ◽  
Uncertain Environments ◽  
Robot Systems ◽  
Markov Decision ◽  
Partially Observable ◽  
Multi Robot

Optimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera. In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We first describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decentralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs.

Optimal distributed interconnectivity of multi-robot systems by spatially-constrained clustering

2017 ◽  
Vol 25 (2) ◽  
pp. 96-113 ◽  
Author(s):  
Matin Macktoobian ◽  
Mahdi Aliyari Sh
Keyword(s):  
Clustering Algorithm ◽  
Task Assignment ◽  
Constrained Clustering ◽  
Loosely Coupled ◽  
Robot Systems ◽  
Multi Agent ◽  
Probabilistic Proof ◽  
Data Passing ◽  
Spatially Constrained Clustering ◽  
Multi Robot

A spatially-constrained clustering algorithm is presented in this paper. This algorithm is a distributed clustering approach to fine-tune the optimal distances between agents of the system to strengthen the data passing among them using a set of spatial constraints. In fact, this method will increase interconnectivity among agents and clusters, leading to improvement of the overall communicative functionality of the multi-robot system. This strategy will lead to the establishment of loosely-coupled connections among the clusters. These implicit interconnections will mobilize the clusters to receive and transmit information within the multi-agent system. In other words, this algorithm classifies each agent into the clusters with the lowest cost of local communication with its peers. This research demonstrates that the presented decentralized method will actually boost the communicative agility of the swarm by probabilistic proof of the acquired optimality. Hence, the common assumption regarding the full-knowledge of the agents’ primary locations has been fully relaxed compared to former methods. Consequently, the algorithm’s reliability and efficiency is confirmed. Furthermore, the method’s efficacy in passing information will improve the functionality of higher-level swarm operations, such as task assignment and swarm flocking. Analytical investigations and simulated accomplishments, corresponding to highly-populated swarms, prove the claimed efficiency and coherence.

Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions

2017 ◽  
Vol 36 (2) ◽  
pp. 231-258 ◽  
Author(s):  
Shayegan Omidshafiei ◽  
Ali–Akbar Agha–Mohammadi ◽  
Christopher Amato ◽  
Shih–Yuan Liu ◽  
Jonathan P How ◽  
...  
Keyword(s):  
Markov Decision Processes ◽  
Large Scale ◽  
Decision Processes ◽  
Delivery Problem ◽  
Package Delivery ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
High Level ◽  
Partially Observable ◽  
Multi Robot

This work focuses on solving general multi-robot planning problems in continuous spaces with partial observability given a high-level domain description. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems. However, representing and solving Dec-POMDPs is often intractable for large problems. This work extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) to take advantage of the high-level representations that are natural for multi-robot problems and to facilitate scalable solutions to large discrete and continuous problems. The Dec-POSMDP formulation uses task macro-actions created from lower-level local actions that allow for asynchronous decision-making by the robots, which is crucial in multi-robot domains. This transformation from Dec-POMDPs to Dec-POSMDPs with a finite set of automatically-generated macro-actions allows use of efficient discrete-space search algorithms to solve them. The paper presents algorithms for solving Dec-POSMDPs, which are more scalable than previous methods since they can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed algorithms are then evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent realistic problems and provide high-quality solutions for large-scale problems.

scholarly journals FRAMEWORK FOR AD HOC NETWORK COMMUNICATION IN MULTI-ROBOT SYSTEMS

2016 ◽  
Vol 6 ◽  
pp. 18
Author(s):  
Khilda Slyusar ◽  
Miroslav Kulich
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
A Priori ◽  
Software Framework ◽  
Exploration Process ◽  
Robot Systems ◽  
Mobile Ad Hoc ◽  
Hoc Networks ◽  
Multi Robot

Assume a team of mobile robots operating in environments where no communication infrastructure like routers or access points is available. The robots have to create a mobile ad hoc network, in that case, it provides communication on peer-to-peer basis. The paper gives an overview of existing solutions how to route messages in such ad hoc networks between robots that are not directly connected and introduces a design of a software framework for realization of such communication. Feasibility of the proposed framework is shown on the example of distributed multi-robot exploration of an a priori unknown environment. Testing of developed functionality in an exploration scenario is based on results of several experiments with various input conditions of the exploration process and various sizes of a team and is described herein.

scholarly journals 6G Mobile Communications for Multi-Robot Smart Factory

2021 ◽  
Author(s):  
Zhenyi Chen ◽  
Kwang-Cheng Chen ◽  
Chen Dong ◽  
Zixiang Nie
Keyword(s):  
Mobile Communication ◽  
Mobile Communications ◽  
Task Assignment ◽  
Wireless Networking ◽  
Smart Factory ◽  
Efficient Operation ◽  
Technical Requirements ◽  
Robot Systems ◽  
Robot Task ◽  
Multi Robot

Private or special-purpose wireless networks present a new technological trend for future mobile communications, while one attractive application scenario is the wireless communication in a smart factory. In addition to wireless technologies, this paper pays special attention to treat a smart factory as the integration of collaborative multi-robot systems for production robots and transportation robots. Multiple aspects of collaborative multi-robot systems enabled by wireless networking have been investigated, dynamic multi-robot task assignment for collaborative production robots and subsequent transportation robots, social learning to enhance precision and robustness of collaborative production robots, and more efficient operation of collaborative transportation robots. Consequently, the technical requirements of 6G mobile communication can be logically highlighted.

Exploration Strategies of Coordinated Multi-Robot System: A Comparative Study

2018 ◽  
Vol 7 (1) ◽  
pp. 48
Author(s):  
Ayman. El shenawy ◽  
Khalil. Mohamed ◽  
Hany. M. Harb
Keyword(s):  
Comparative Study ◽  
Process Performance ◽  
Basic Process ◽  
Robot System ◽  
Exploration Process ◽  
Coordination Strategy ◽  
System A ◽  
Environment Exploration ◽  
Robot Systems ◽  
Multi Robot

Environment Exploration is the basic process that most of Multi Robot Systems applications depend on it. The exploration process performance depends on the coordination strategy between the robots participating in the team.  In this paper the coordination of Multi Robot Systems in the exploration process is surveyed, and the performance of different Multi Robot Systems exploration strategies is contrasted and analyzed for different environments and different team sizes.

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