A survey of Model Driven Engineering in robotics

Silva, Edson de Araújo; Valentin, Eduardo; Carvalho, José Oscar Fontanini de; Barreto, Raimundo

doi:10.1016/j.cola.2020.101021

Cited by 34 publications

(27 citation statements)

References 37 publications

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“…Note that Allocation 5 is the optimal solution in the case of single-objective CAP optimization. Figure 2 x (1) x (2) x (3) x (4) x (5) x (6) x (7) x (8) Feasible Infeasible Non-Dominated…”

Section: Component Allocation Problemmentioning

confidence: 99%

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A Model-Driven Approach for Solving the Software Component Allocation Problem

2021

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The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units could bring many benefits, such as improved quality of service (QoS), reduced energy consumption, reduction of costs, and many others. Therefore, in this paper, we introduce a model-based framework that aims to solve the software component allocation problem (CAP). We formulate it as an optimization problem with either single or multiple objective functions and cover both cases in the proposed framework. Additionally, our framework also provides visualization and comparison of the optimal solutions in the case of multi-objective component allocation. The main contributions introduced in this paper are: (1) a novel methodology for tackling CAP-alike problems based on the usage of model-driven engineering (MDE) for both problem definition and solution representation; (2) a set of Python tools that enable the workflow starting from the CAP model interpretation, after that the generation of optimal allocations and, finally, result visualization. The proposed framework is compared to other similar works using either linear optimization, genetic algorithm (GA), and ant colony optimization (ACO) algorithm within the experiments based on notable papers on this topic, covering various usage scenarios—from Cloud and Fog computing infrastructure management to embedded systems, robotics, and telecommunications. According to the achieved results, our framework performs much faster than GA and ACO-based solutions. Apart from various benefits of adopting a multi-objective approach in many cases, it also shows significant speedup compared to frameworks leveraging single-objective linear optimization, especially in the case of larger problem models.

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“…Note that Allocation 5 is the optimal solution in the case of single-objective CAP optimization. Figure 2 x (1) x (2) x (3) x (4) x (5) x (6) x (7) x (8) Feasible Infeasible Non-Dominated…”

Section: Component Allocation Problemmentioning

confidence: 99%

“…By means of model transformation, a model conforming to a meta-model can be automatically transformed into another target model conforming to a new meta-model. Advances in MDE have been applied in a wide variety of domains [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Model-Driven Approach for Solving the Software Component Allocation Problem

2021

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“…The majority of the proposed domain-specific modelling languages deals only with specific robot functions such as perception or control, while there are some model-driven toolchains like RobotML [10], BRICS [12], SmartSoft [9], and Robochart [11] which provide multiple modelling notations to be used together when developing a robotic system. For a detailed description of different approaches to model-driven engineering of robots, the reader is referred to [18] and [33].…”

Section: Related Workmentioning

confidence: 99%

Model-Driven Simulation-Based Analysis for Multi-Robot Systems

Harbin

Gerasimou

Matragkas

et al. 2021

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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“…The majority of existing robotics MDE approaches use internal DSMLs (relying on the syntax and the execution semantics of their host languages) [2]. Internal DSLs are based on GPMLs, such as UML [3] (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Internal DSLs are based on GPMLs, such as UML [3] (e.g. RobotML [4]), SysML [5], graphical DSMLs as used by SmartSoft [6]), and most popular in robotics Ecore (32 MDE approaches out of 63 analysed in [2] have used DSMLs based on Ecore. Internal DSMLs are bound to the execution context [7], i.e.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Robotic System Controllers Using Robotic System Specification Language

Figat

Zieliński

2023

IEEE Robot. Autom. Lett.

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Robotic System Specification Language (RSSL) stems from the embodied agent approach to robotic system design. It enables the specification of both the structure and activities of a multi-robot multi-agent robotic system. RSSL specification can be verified and automatically transformed by its compiler into a six-layered Robotic System Hierarchical Petri Net (RSHPN). RSHPN models the activities and structure of the designed robotic system. The automatically generated RSHPN is loaded into RSHPN Tool modeling RSHPNs and automatically generating the controller code. This approach was validated on several robotic systems. The use of RSSL and RSHPN facilitates the synthesis of robotic system controllers.

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A survey of Model Driven Engineering in robotics

Cited by 34 publications

References 37 publications

A Model-Driven Approach for Solving the Software Component Allocation Problem

A Model-Driven Approach for Solving the Software Component Allocation Problem

Model-Driven Simulation-Based Analysis for Multi-Robot Systems

Synthesis of Robotic System Controllers Using Robotic System Specification Language

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