Multi-Robot Coverage and Persistent Monitoring in Sensing-Constrained Environments

Alam, Tauhidul; Bobadilla, Leonardo

doi:10.3390/robotics9020047

Cited by 12 publications

(14 citation statements)

References 38 publications

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“…To construct a graph 𝐺 𝑟 for the seven robots RA, RB, RC, RD, RE, RF, and RG, their connectivity is determined based on (1). In the graph, the robots correspond to 𝑉 𝑟 (1), 𝑉 𝑟 (2), …, and 𝑉 𝑟 (7), respectively.…”

Section: A Construction Of Robot Relational Graphmentioning

confidence: 99%

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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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Section: A Construction Of Robot Relational Graphmentioning

confidence: 99%

“…With the increase in unmanned surveillance systems, the necessity of using multiple robots has increased [1][2][3][4][5]. The essence of the multi-robot surveillance system (MRSS) is for multiple robots to completely cover a given area within a limited time.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

2023

IEEE Access

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“…Starting the full information phase from any of the points on this engagement surface guarantees capture of the intruder since the Apollonius circles generated from these engagement configurations do not have any intersection with the target perimeter or the TSR boundary. This can be verified by constructing the Apollonius circle as per (3) and using Assumption (2). Given that the capture is guaranteed if engagement is started from this surface, the intruder's objective in this case is to get captured the farthest from the target center.…”

Section: A Partial Information Phasementioning

confidence: 99%

“…In this paper, we study a circular target-defence game between a single defender and a team of sequentially arriving intruders. This problem has been found useful in multi-robot applications such as patrolling [1], area monitoring [2], area securing [3], coastline defense [4] and has motivated a great amount of research e.g., see [5] for a review on this topic.…”

Section: Introductionmentioning

confidence: 99%

Target Defense against Sequentially Arriving Intruders

Pourghorban

Dorothy

Shishika

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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We consider a variant of pursuit-evasion games where a single defender is tasked to defend a static target from a sequence of periodically arriving intruders. The intruders' objective is to breach the boundary of a circular target without being captured and the defender's objective is to capture as many intruders as possible. At the beginning of each period, a new intruder appears at a random location on the perimeter of a fixed circle surrounding the target and moves radially towards the target center to breach the target. The intruders are slower in speed compared to the defender and they have their own sensing footprint through which they can perfectly detect the defender if it is within their sensing range. Considering the speed and sensing limitations of the agents, we analyze the entire game by dividing it into partial information and full information phases. We address the defender's capturability using the notions of engagement surface and capture circle. We develop and analyze three efficient strategies for the defender and derive a lower bound on the capture fraction. Finally, we conduct a series of simulations and numerical experiments to compare and contrast the three proposed approaches.

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“…Multi-robot systems are making a significant impact on fundamental societal areas. From oceanic exploration to border surveillance, from robotic warehousing to precision agriculture, and from automated construction to environmental monitoring, collaborating groups of robots will play a central role in the coming years [1]. In some of these scenarios, however, due to technical, ethical, regulatory or safety issues [2], one or more humans should monitor or help the robot during the execution of its tasks in certain critical parts .…”

Section: Introductionmentioning

confidence: 99%

Scheduling and Path-Planning for Operator Oversight of Multiple Robots

et al. 2021

Self Cite

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There is a need for semi-autonomous systems capable of performing both automated tasks and supervised maneuvers. When dealing with multiple robots or robots with high complexity (such as humanoids), we face the issue of effectively coordinating operators across robots. We build on our previous work to present a methodology for designing trajectories and policies for robots such that a few operators can supervise multiple robots. Specifically, we: (1) Analyze the complexity of the problem, (2) Design a procedure for generating policies allowing operators to oversee many robots, (3) Present a method for designing policies and robot trajectories to allow operators to oversee multiple robots, and (4) Include both simulation and hardware experiments demonstrating our methodologies.

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Multi-Robot Coverage and Persistent Monitoring in Sensing-Constrained Environments

Cited by 12 publications

References 38 publications

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

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

Target Defense against Sequentially Arriving Intruders

Scheduling and Path-Planning for Operator Oversight of Multiple Robots

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