Multi-robot sensor-based coverage path planning using capacitated arc routing approach

Parlaktuna, Osman; Sipahioglu, Aydin; Kirlik, Gokhan; Yazıcı, Ahmet

doi:10.1109/cca.2009.5281159

Cited by 7 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first case can be solved using with MUA proposed in [7]. This case usually can be faced during the initial plan where robots are fully charged and in the same depot.…”

Section: The Proposed Approachmentioning

confidence: 99%

“…Hence, different heuristic algorithms are developed in the literature such as Simple constructive methods (Construct-Strike, ModifiedConstruct-Strike, Path-Scanning, Augment-Merge algorithm, Parallel Insert Algorithm, Augment-Insert Algorithm), two-phase constructive methods (UlusoyPartitioning algorithm [12], Cut Algorithm and Cycle Assignment algorithm [13]), and metaheuristics (Tabu search-based algorithms [14] and genetic algorithms [15]). In [7], a minor modification was done on Ulusoy's partitioning algorithm to handle multi-robot sensor-based coverage path planning problem for the same depot point and equal energy capacity. Assume that ij e denotes energy consumption during the travel through edge due to motors, micro controller units, etc., and ij q denotes required energy for sensor coverage and traveling on the edge ) , (…”

Section: Preliminaries and Algorithms Used In The Proposed Approachmentioning

confidence: 99%

“…Because of this difference, CARP solution techniques can not be used directly for the sensor-based coverage problem with multiple robots. In [7], this difference between the classical CARP problem and the coverage problem explained and solved by some minor modification to Ulusoy's algorithm that is used for classical CARP. The method proposed in [7] was used to construct initial coverage routes for a robot team under the assumption that the robots start from the same depot point and they have equal energy capacities (all fully charged).…”

Section: Introductionmentioning

confidence: 99%

“…In [7], this difference between the classical CARP problem and the coverage problem explained and solved by some minor modification to Ulusoy's algorithm that is used for classical CARP. The method proposed in [7] was used to construct initial coverage routes for a robot team under the assumption that the robots start from the same depot point and they have equal energy capacities (all fully charged). But, if robots face with a blocking obstacle due to partially unknown nature of the environment, a fast re-planning is required.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A dynamic path planning approach for multi-robot sensor-based coverage considering energy constraints

Yazıcı¹,

Kirlik²,

Parlaktuna³

et al. 2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

In this study, a novel dynamic path planning approach is proposed for multi-robot sensor-based coverage considering energy capacities of the mobile robots. The environment is assumed to be narrow and partially unknown. A Generalized Voronoi diagram-based network is used for the sensor-based coverage planning due to narrow nature of the environment. On the other hand, partially unknown nature is handled with proposed dynamic re-planning approach. Initially, the robots are assumed to be at the same depot with equal initial energy capacities. In this case, an initial complete coverage route is constructed considering robot energy capacities using classical capacitated arc routing problem (CARP) approach with some minor modifications related to coverage problem. But, due to partially unknown nature, the robots may face with blockage on routes, and a fast re-planning is required which considers remaining energy capacities and current positions of the robots. So, new plan is obtained by a modifying Ulusoy's algorithm that was developed for classical CARP. The developed algorithm is coded in C++ and implemented on P3-DX mobile robots in MobileSim simulation environment.

show abstract

“…The first case can be solved using with MUA proposed in [7]. This case usually can be faced during the initial plan where robots are fully charged and in the same depot.…”

Section: The Proposed Approachmentioning

confidence: 99%

Section: Preliminaries and Algorithms Used In The Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A dynamic path planning approach for multi-robot sensor-based coverage considering energy constraints

Yazıcı¹,

Kirlik²,

Parlaktuna³

et al. 2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Series of research were conducted related to control algorithm and hardware configuration [17] [18], as well as group of robots [19] and robots formations [20]. Relevant to this research is robots collaboration [21] and multi-robots networks [22] [23] [24]. There is proposed generic algorithm [25] and reference generation and control schemes [26].…”

Section: Recent Developments and Research Backgrounmentioning

confidence: 99%

Coefficient of agility and sampling frequency issues in Mobile Agents Collision Detection with Dynamic Obstacles in 3D Space

Babovic

2012

2012 12th IEEE International Workshop on Advanced Motion Control (AMC)

View full text Add to dashboard Cite

This research is extension of previous research on method of Collaborative and Non-Collaborative Dynamic Path Prediction Algorithm for Mobile Agents Collision Detection withDynamic Obstacles in 3D Space. In this research the extension of the algorithm for dynamic collaborative path prediction for mobile agents is proposed and two important issues Coefficient of agility and Minimal sampling frequency are analyzed. Those two terms are proposed in previous research for which background and introduction is explained in this paper. Solving Coefficient of agility and minimal sampling frequency issues allows system designers and developers to implement the method. This method allows full decentralization of collision detection which allows many advantages from minimizing of network traffic to simplifying of inclusion of additional agents in relevant space. Implementation of the algorithm will be low resource consuming allowing mobile agents to free resources for additional tasks.

show abstract

Collaborative and Non-Collaborative Dynamic Path Prediction Algorithm for Mobile Agents Collision Detection with Dynamic Obstacles in 3D Space

Babovic

2012

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Multi-robot sensor-based coverage path planning using capacitated arc routing approach

Cited by 7 publications

References 18 publications

A dynamic path planning approach for multi-robot sensor-based coverage considering energy constraints

A dynamic path planning approach for multi-robot sensor-based coverage considering energy constraints

Coefficient of agility and sampling frequency issues in Mobile Agents Collision Detection with Dynamic Obstacles in 3D Space

Collaborative and Non-Collaborative Dynamic Path Prediction Algorithm for Mobile Agents Collision Detection with Dynamic Obstacles in 3D Space

Contact Info

Product

Resources

About