Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

Palmieri, N.; Yang, Xin-She; Rango, Floriano De; Santamaria, Amilcare Francesco

doi:10.1016/j.neucom.2018.03.038

Cited by 27 publications

(11 citation statements)

References 45 publications

(68 reference statements)

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“…The robots' recruitment time and the overall area exploring time have both been regarded as reduction targets. Palmieri et al [31] presented a restricted bi-objective optimisation problem in which mobile robots must undertake two distinct tasks of exploration while also cooperating and coordinating to disarm the dangerous targets. These are diametrically opposite aims, with one valued only at the expense of the other.…”

Section: Related Workmentioning

confidence: 99%

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Akram

Munawar

Kouzani

et al. 2022

Sensors

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Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

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Section: Related Workmentioning

confidence: 99%

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Akram

Munawar

Kouzani

et al. 2022

Sensors

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“…The model of MA usually includes the library of procedures (the experience of the system in the form of actions) [6][7][8], inference drive (problem-solver) [9][10][11][12], knowledge base (more general than procedures), ana a perception processor with the possibility to communicate with the sensors of robots [11,13]. MA concepts have found their implementations for robotic group tasks [13][14][15][16][17], for manufacturing problems [18][19][20][21][22], and also for social and collaborative robotic problems [23][24][25][26][27]. The simulation component allows the estimation of possible results for the activity of MA [13,14].…”

Section: Agent-based Systems and Role Of Robotsmentioning

confidence: 99%

Predicate-Based Model of Problem-Solving for Robotic Actions Planning

2021

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The aim of the article is to describe a predicate-based logical model for the problem-solving of robots. The proposed article deals with analyses of trends of problem-solving robotic applications for manufacturing, especially for transportations and manipulations. Intelligent agent-based manufacturing systems with robotic agents are observed. The intelligent cores of them are considered from point of view of ability to propose the plans of problem-solving in the form of strategies. The logical model of adaptive strategies planning for the intelligent robotic system is composed in the form of predicates with a presentation of data processing on a base of set theory. The dynamic structures of workspaces, and a possible change of goals are considered as reasons for functional strategies adaptation.

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“…This has the benefit of enduing the allocation framework with a degree of resilience, as will be demonstrated in Section IV-C. Indeed, adaptivity and resilience are commonly studied aspects of task allocation in multi-robot systems (see, e.g., [6], [28], [29], [30]). Typically, adaptivity is incorporated by defining a time-varying propensity of robots to participate in different tasks.…”

Section: B Related Workmentioning

confidence: 99%

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

Notomista¹,

Mayya²,

Emam³

et al. 2021

Preprint

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In the context of heterogeneous multi-robot teams deployed for executing multiple tasks, this paper develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Towards this end, the task prioritization and execution-through which the allocation of tasks to robots is effectively realizedare encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots, and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.

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Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

Cited by 27 publications

References 45 publications

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Predicate-Based Model of Problem-Solving for Robotic Actions Planning

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

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