Multi-robot rendezvous based on bearing-aided hierarchical tracking of network topology

Luo, Shuai; Kim, Jonghoek; Parasuraman, Ramviyas; Bae, Jun Han; Matson, Eric T.; Min, Byung‐Cheol

doi:10.1016/j.adhoc.2018.11.004

Cited by 32 publications

(19 citation statements)

References 22 publications

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“…Mobility: for systems characterized by mobile devices such as smartphones, vehicles, robots, or drones, the TA strategy has to be designed so that it can easily adapt to topology changes, also integrating device mobility tracking and prediction [16] [17]. Speed-related issues need to be considered: devices whose speed is very high (e.g.…”

Section: Factors Impacting On Task Allocationmentioning

confidence: 99%

“…Indeed, such approaches are particularly suitable to model TA problems where a specific cost can be associated with each involved device. This is the case, for instance, of trajectory planning problems, which are typical of MCS [87][88], MRS [16], and UAV [89], and are often solved using graph-based approaches (e.g. Traveling Salesman Problem, Traveling Repairman Problem) [90] [91].…”

Section: B Approaches Typically Included In Task Allocation Algorithmsmentioning

confidence: 99%

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Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Pilloni

Ning

Atzori

2022

IEEE Internet Things J.

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Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-ofthe-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities' provisioning.

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Section: Factors Impacting On Task Allocationmentioning

confidence: 99%

Section: B Approaches Typically Included In Task Allocation Algorithmsmentioning

confidence: 99%

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Pilloni

Ning

Atzori

2022

IEEE Internet Things J.

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“…We utilize a local monocular vision sensor with which each robot detects and tracks the spill boundary. However, the neighboring robot collision avoidance can be achieved using sonar [4], radar, or lidar sensors with 360 degrees scanning [16]. Therefore, the vision field or blind zone of vision sensors will not hinder the robot collision avoidance.…”

Section: Related Workmentioning

confidence: 99%

“…We do not assume a global view of the workspace; instead, the robot team will first perform a random walk until each spill has been detected by at least one robot [3]. For each spill, the nearest robot becomes a rendezvous point for other robots by way of wireless connectivity mediated through on-board communication modules [4]. When any robot finishes migrating toward the rendezvous point to which it is allocated, this robot will begin boundary shrink control.…”

mentioning

confidence: 99%

Asymptotic Boundary Shrink Control With Multirobot Systems

Luo

Kim

Min

2022

IEEE Trans. Syst. Man Cybern, Syst.

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Harmful marine spills, such as algae blooms and oil spills, damage ecosystems and threaten public health tremendously. Hence, an effective spill coverage and removal strategy will play a significant role in environmental protection. In recent years, low-cost water surface robots have emerged as a solution, with their efficacy verified at small scale. However, practical limitations such as connectivity, scalability, and sensing and operation ranges significantly impair their large-scale use. To circumvent these limitations, we propose a novel asymptotic boundary shrink control strategy that enables collective coverage of a spill by autonomous robots featuring customized operation ranges. For each robot, a novel controller is implemented that relies only on local vision sensors with limited vision range. Moreover, the distributedness of this strategy allows any number of robots to be employed without inter-robot collisions. Finally, features of this approach including the convergence of robot motion during boundary shrink control, spill clearance rate, and the capability to work under limited ranges of vision and wireless connectivity are validated through extensive experiments with simulation.Index Terms-Multi-robot coordination, Spill removal, Boundary shrink control, Distributed control, Artificial potential field I. INTRODUCTIONC OVERAGE operation has a wide application scope in environment searching and exploration. Currently, methods of coverage control have seen tremendous potential in solving environmental issues caused by side effects of urbanization and industrialization. One global issue of increasing importance is water pollution, including harmful algae blooms that result from eutrophication, and oil leakage in the oceans during transportation, due to its connections with potable water supplies and even marine ecosystems. It is a natural idea to deploy a team of water surface robots for cleaning such pollution, in order to protect human operators and to provide for immediate operation response before any ecological or economic damage results.Researchers from MIT developed one approach for the removal of oil spills in the ocean: Seaswarm, a fleet of low-cost, oil-absorbing robots [1]. These robots are powered by solar cells and designed to move on the water surface, absorbing oil spills through the long edge of an attached conveyor belt, as shown in Fig. 1. The Seaswarm robot Manuscript received...

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“…The ubiquitous availability of RSSI measurement on such inexpensive commercial devices is the Received Signal Strength Indicator (RSSI) measured from an Access Point (AP) or a Wireless Sensor Node (WSN). This RSSI values can be used in various applications, including localization [5]- [7], control [8], and communication optimization [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Online Indoor Localization Using DOA of Wireless Signals

Latif¹,

Parasuraman²

2022

Preprint

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Localization of a wireless mobile device or a robot in indoor and GPS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional cameras and LIDAR-based alternative sensing and localization modalities may fail. We propose a method for estimating the location of a mobile robot in relation to static wireless sensor nodes (WSN) deployed in the environment. The method employs a novel particle filter that updates its weights using a Gauss probability over Direction of Arrival (DOA) estimate in conjunction with the mobile robot's mobility model. We evaluate and validate the proposed method in terms of accuracy and computational efficiency through extensive simulations and public realworld measurement datasets, comparing with standard state-of-the-art localization approaches. The results show considerably high meter-level localization accuracy balanced by the high computational efficiency, enabling it to use online without a need for a dedicated offline phase as in typical fingerprint-based localization algorithms.

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Multi-robot rendezvous based on bearing-aided hierarchical tracking of network topology

Cited by 32 publications

References 22 publications

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Asymptotic Boundary Shrink Control With Multirobot Systems

Online Indoor Localization Using DOA of Wireless Signals

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