Multi-robot System for Vacuum Cleaning Domain

Ņikitenko, Agris; Grundspeņķis, Jānis; Liekna, Aleksis; Ekmanis, Martins; Kuļikovskis, Guntis; Andersone, Ilze

doi:10.1007/978-3-319-07551-8_39

Cited by 8 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prime examples of its benefits include [3]- [13]. [7] uses rapidly exploring random tree (RRT) for vacuum robot navigation and path planning for large indoor vacuum cleaning. [5] by Connell et al uses the RRT x for path replanning in a non-static environment.…”

Section: Related Workmentioning

confidence: 99%

“…6 Initialize V start , the starting Vertex and set it to null. 7 Initialize a vertex array Q, of the dirt model where each vertex is a cell: 8 Vertex V i has a coordinate (x, y), a dirt level λ, a boolean visit that is true when V start is connected or pass through it and an array of vertex it is connected to. 9 Initialize integer n and set it to null.…”

Section: Path Planningmentioning

confidence: 99%

See 1 more Smart Citation

A Multi-Robotic System for Environmental Dirt Cleaning

Pham

La³

et al. 2020

2020 IEEE/SICE International Symposium on System Integration (SII)

View full text Add to dashboard Cite

There is a lot of waste in an industrial environment that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automative cleaning robots could be used, the environment is often too big for one robot to clean alone in addition to the fact that it does not have adequate stored dirt capacity. We present a multi-robotic dirt cleaning system algorithm for multiple automatic iRobot Creates teaming to efficiently clean an environment. Moreover, since some spaces in the environment are clean while others are dirty, our multi-robotic system possesses a path planning algorithm to allow the robot team to clean efficiently by spending more time on the area with higher dirt level. Overall, our multi-robotic system outperforms the single robot system in time efficiency while having almost the same total battery usage and cleaning efficiency result.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Path Planningmentioning

confidence: 99%

A Multi-Robotic System for Environmental Dirt Cleaning

Pham

La³

et al. 2020

2020 IEEE/SICE International Symposium on System Integration (SII)

View full text Add to dashboard Cite

show abstract

“…Recently, multi-cleaning robots have become an important research topic in the field of cleaning robots [1]. Most cleaning robots are used in small environments such as houses; however, there is a growing need for robots that can be used in large environments such as libraries, airports, playgrounds, and warehouses [2]. When cleaning a sizable area using a single robot, the cleaning time is prolonged owing to the slow cleaning progress and the low battery capacity [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Lee

Kang

2020

IEEE Access

View full text Add to dashboard Cite

Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports. Cleaning large environments with multiple robots is faster than cleaning them with a single robot. Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots. However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots. Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process. In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots. The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles. Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots. INDEX TERMS Coverage path planning, cleaning robot, computational efficiency, multi-robots system, robot path planning.

show abstract

“…Several applications of autonomous multirobot systems require to perform some form of coverage, namely to visit all the locations of given environments. Examples include cleaning [18], industrial inspection [10], patrolling [20], and precision agriculture [3]. The coverage problem has been widely studied in several variants [7,13].…”

Section: Introductionmentioning

confidence: 99%

Multirobot Coverage of Linear Modular Environments

Salaris,

Riva,

Amigoni

2020

Preprint

View full text Add to dashboard Cite

Multirobot systems for covering environments are increasingly used in applications like cleaning, industrial inspection, patrolling, and precision agriculture. The problem of covering a given environment using multiple robots can be naturally formulated and studied as a multi-Traveling Salesperson Problem (mTSP). In a mTSP, the environment is represented as a graph and the goal is to find tours (starting and ending at the same depot) for the robots in order to visit all the vertices with minimum global cost, which is typically calculated as the makespan, namely the length of the longest tour. The mTSP is an NP-hard problem for which several approximation algorithms have been proposed. These algorithms usually assume generic environments, but tighter approximation bounds can be reached focusing on specific environments. In this paper, we address the case of modular environments, namely of environments composed of sub-parts, called modules, that can be reached from each other only through some linking structures. Examples are multifloor buildings, in which the modules are the floors and the linking structures are the staircases or the elevators, and floors of large hotels or hospitals, in which the modules are the rooms and the linking structures are the corridors. We focus on linear modular environments, with the modules organized sequentially, presenting an efficient (with polynomial worst-case time complexity) algorithm that finds a solution for the mTSP whose cost is within a bounded distance from the cost of the optimal solution. The main idea of our algorithm is to allocate disjoint "blocks" of adjacent modules to the robots, in such a way that each module is covered by only one robot. We experimentally compare our algorithm against some state-of-the-art algorithms for solving mTSPs in generic environments and show that it is able to provide solutions with lower makespan and spending a computing time several orders of magnitude shorter.

show abstract

Multi-robot System for Vacuum Cleaning Domain

Cited by 8 publications

References 3 publications

A Multi-Robotic System for Environmental Dirt Cleaning

A Multi-Robotic System for Environmental Dirt Cleaning

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Multirobot Coverage of Linear Modular Environments

Contact Info

Product

Resources

About