Decentralized and complete multi-robot motion planning in confined spaces

Wiktor, Adam; Scobee, Dexter R. R.; Messenger, Sean; Clark, Christopher M.

doi:10.1109/iros.2014.6942705

Cited by 17 publications

(18 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuzzy rules for determining the traversal cost of cells in the map are constructed as in Table 1. Fuzzy logic techniques are efficient in solving complex, ill-defined problems that are characterized by uncertainty of environment and fuzziness of information [36,37]. Taking into account that disturbances and noises are common sources of uncertainties, it can be concluded that from the aspect of fuzzy implementation this system is highly resistant to noise and disturbance [38,39,40].…”

Section: Figurementioning

confidence: 99%

“…Generally speaking, there are two approaches that can be applied to algorithms designed for planning the path of a multi-robot system [36]. The first approach involves a coupled technique that treats the multi-robot system as a single entity so that the paths of all robots of that system are calculated simultaneously and coordinated in the joint configuration space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Zagradjanin

Rodić

Pamučar

et al. 2021

ITC

View full text Add to dashboard Cite

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

show abstract

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Zagradjanin

Rodić

Pamučar

et al. 2021

ITC

View full text Add to dashboard Cite

show abstract

“…area that has gained attention by many researchers over past few years [1][2]. This type of system gives authority to every single member to make decision on its own.…”

Section: Ecentralized Robotic System Is An Effectivementioning

confidence: 99%

De-Centralized Multi Robot Co-Ordination and Communication

Khan

Athar

2019

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

The aim of this research is to develop a decentralized communication system of multiple robots which have capability to communicate and coordinate with each other, by which all robots work individually while keeping connectivity among them. Decentralized communication between robots is the main part of this project. This type of communication shows great potential in many aspects. A decentralized system is the combination of both intelligent and decision making inspired from nature. To meet this object two identical robots are developed which successfully communicate with each other to complete an assigned task. A single task is given to both robots; to find specific object/target location. Once the target is found by any of the robot that robot will then communicate with second robot to guide the path of object/target location. Guiding path to the second robot is the challenging task as discussed in this paper. The Robots use Bluetooth devices to calculate and extract the RSSI (Received Signal Strength Indication)values for communication purpose.Both robots work together as a team. By working in communicated way their time is reduced to achieve the task and create synergy. Any task could be easily attained in team work than by the work of the single individual robot.

show abstract

“…The existing methods for multi-robot path planning, from the aspect of implemented algorithms, can be divided into two general approaches [3]. The coupled approach regards the group of robots as a single entity, such that all paths are planned simultaneously in a joint or composite configuration space and therefore could guarantee completeness, but these solutions do not scale well with large robot teams and they usually cannot be solved in real-time.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

2019

View full text Add to dashboard Cite

The progress in the research of various areas of robotics, artificial intelligence, and other similar scientific disciplines enabled the application of multi-robot systems in different complex environments and situations. It is necessary to elaborate the strategies regarding the path planning and paths coordination well in order to efficiently execute a global mission in common environment, prior to everything. This paper considers the multi-robot system based on the cloud technology with a high level of autonomy, which is intended for execution of tasks in a complex and crowded environment. Cloud approach shifts computation load from agents to the cloud and provides powerful processing capabilities to the multi-robot system. The proposed concept uses a multi-robot path planning algorithm that can operate in an environment that is unknown in advance. With the aim of improving the efficiency of path planning, the implementation of Multi-criteria decision making (MCDM) while using Full consistency method (FUCOM) is proposed. FUCOM guarantees the consistent determination of the weights of factors affecting the robots motion to be symmetric or asymmetric, with respect to the mission specificity that requires the management of multiple risks arising from different sources, optimizing the global cost map in that way.

show abstract

Decentralized and complete multi-robot motion planning in confined spaces

Cited by 17 publications

References 8 publications

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

De-Centralized Multi Robot Co-Ordination and Communication

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

Contact Info

Product

Resources

About