Balanced partitioning of workspace for efficient multi-robot coordination

Gautam, Avinash; Ram, Shri; Shekhawat, Virendra Singh; Mohan, Sudeept

doi:10.1109/robio.2017.8324402

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Another significant problem in the coordination of multi-robot systems is to maximize the degree of balance in the workload allocation within the multi-robot team [48], [49]. The multi-robot system should be displayed to its actual potential by load balancing.…”

Section: B Weighted Voronoi Diagram-based Area Partitionmentioning

confidence: 99%

A Voronoi Diagram-Based Workspace Partition for Weak Cooperation of Multi-Robot System in Orchard

Kim

Son

2020

IEEE Access

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Nowadays, multi-robot systems are being utilized to perform agricultural tasks. It is particularly essential to robotize pesticide spraying because of the risk of poisoning workers. However, the problem is that the human-driven sprayers are big size, and difficult to maneuver in many orchards. Therefore, heavy spraying robots must be replaced with lighter robots. Also, it takes a lot of times to perform spraying using only one sprayer in a large orchard. To achieve this goal, the multi-robot system (MRS) can be applied to the spraying by robot cooperation to improve performance. In this study, we developed a task allocation system based on a Voronoi diagram for a multi-robot spraying system in an orchard. The seed point for area partition using the Voronoi diagram was obtained through node clustering using a k-means clustering algorithm. In the experiment, workspaces were partitioned according to the number of robots, from 2 to 10. A total of four metrics were used to evaluate the performance of the system. The results confirmed that our task allocation system is applicable to real orchards. INDEX TERMS Agricultural robot, multi-robot system, task allocation, area partition, unmanned ground vehicle (UGV).

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Section: B Weighted Voronoi Diagram-based Area Partitionmentioning

confidence: 99%

A Voronoi Diagram-Based Workspace Partition for Weak Cooperation of Multi-Robot System in Orchard

Kim

Son

2020

IEEE Access

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“…In this study, initial MCPP was performed in a similar manner. In a study [38], the problem of unbalanced multi-robot coverage was addressed using Voronoi partitioning. The study showed an improvement in terms of workload balance among the robots compared to the KH algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

Lee

2023

IEEE Access

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This study deals with a new strategy of the re-assignment for multi-robot seamless coverage tasks using the concept of propagation in a multi-robot surveillance system (MRSS). In the context of MRSSs, multi-robot coverage tasks play a critical role. These tasks require generating paths for two or more robots to cover an entire area, with the objective of minimizing the time needed to complete the task. However, over time, robots may need to be excluded from coverage missions due to issues such as battery charging or malfunctions. It is important to handle these situations efficiently in order to maintain the completeness and balance of the coverage mission. Typically, it can be resolved by either recomputing the coverage algorithm for the remaining robots or redistributing the coverage task of the excluded robot to its neighbors. However, in the proposed method, the amount of coverage area of the excluded robot is equally and efficiently assigned to the remaining robots. First off, a relational graph between robots and a tree based on the excluded robot are sequentially constructed to necessarily know how the robots are geometrically arranged in the given area centered on the excluded robot. The excluded robot becomes the root of the tree, and the depth of the tree indicates the proximity of the coverage areas. Subsequently, the amount of the original coverage area of the excluded robot can be differently assigned to its nearest neighbor robots according to the size of the subtree. Then, the coverage area of the robots corresponding to the second level of the tree are added from the partial coverage area of their parent robot to keep their coverage area balanced, respectively. The similar process is continuously performed, such as 'propagation', until the re-assignment of the coverage area over the leaf nodes is complete. Finally, balanced coverage area is re-assigned to the remaining robots, which is time-efficiently computed. Simulations were performed on two occupancy grid maps that were acquired from a simultaneous localization and mapping method. The proposed method was evaluated against conventional methods on three factors such as the balanced re-assignment of the coverage area (Balancing), the variation of the individual coverage area before and after the re-assignment process (Seamless Coverage), and the total computational efficiency over time (Time-efficiency). The coverage area was uniformly re-allocated after the proposed method was applied. In addition, the proposed method had a short calculation time and enables seamless coverage even after re-allocation. In the future, probabilistic maps related to the importance rate, accident rate, and crowds in the coverage area will also be taken into consideration.

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“…Various other approaches used for area exploration are, "Voronoi Graph-based decomposition" [63,91]; biologically inspired [215,216]; graph theory [217] and consensus algorithm [218]. In [219], an approach is used based on Petri Net [220] for area exploration, [221] uses partitioning of topological weighted connected graph for terrain coverage such as floor cleaning, [216] based on honey bee swarm-inspired for forging task, [222] based on finite state automata for two heterogeneous robots looking for an object in a possibly cluttered area. Recently some [223][224][225] Machine Learning (ML) based exploration techniques have also been proposed.…”

Section: Exploration and Mappingmentioning

confidence: 99%

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

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Balanced partitioning of workspace for efficient multi-robot coordination

Cited by 8 publications

References 11 publications

A Voronoi Diagram-Based Workspace Partition for Weak Cooperation of Multi-Robot System in Orchard

A Voronoi Diagram-Based Workspace Partition for Weak Cooperation of Multi-Robot System in Orchard

An Efficient Coverage Area Re-Assignment Strategy for Multi-Robot Long-Term Surveillance

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

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