SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

doi:10.3390/s17030569

Cited by 31 publications

(42 citation statements)

References 28 publications

(28 reference statements)

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“…Nonetheless, the ORO knowledge management system highlights the interaction between robots and humans. SWARMs (Smart and Networking Underwater Robots in Cooperation Meshes) Ontology [25] is built to represent and understand the knowledge of unmanned underwater robots to facilitate cooperation between them. The SWARMs ontology in Reference [13] is divided into four domain-specific ontologies (environmental model, vehicle model, communication model, and mission model) and a core ontology which connects the four domain-specific ontologies.…”

Section: Introductionmentioning

confidence: 99%

RTPO: A Domain Knowledge Base for Robot Task Planning

Sun

Chen

2019

Electronics

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Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

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Section: Introductionmentioning

confidence: 99%

RTPO: A Domain Knowledge Base for Robot Task Planning

Sun

Chen

2019

Electronics

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“…However, ORO focuses on human-computer interaction and lacks support for SAR. Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) Ontology [19][20][21] is designed to describe and understand the complex environments of underwater unmanned robots and promote multi-robot cooperation. However, it is only built for underwater unmanned robots.…”

Section: Introductionmentioning

confidence: 99%

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

Sun

Chen

2019

Future Internet

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The search and rescue (SAR) scenario is complex and uncertain where a robot needs to understand the scenario to make smart decisions. Aiming at the knowledge representation (KR) in the field of SAR, this paper builds an ontology model that enables a robot to understand how to make smart decisions. The ontology is divided into three parts, namely entity ontology, environment ontology, and task ontology. Web Ontology Language (OWL) is adopted to represent these three types of ontology. Through ontology and Semantic Web Rule Language (SWRL) rules, the robot infers the tasks to be performed according to the environment state and at the same time obtains the semantic information of the victims. Then, the paper proposes an ontology-based algorithm for task planning to get a sequence of atomic actions so as to complete the high-level inferred task. In addition, an indoor experiment was designed and built for the SAR scenario using a real robot platform—TurtleBot3. The correctness and usability of the ontology and the proposed methods are verified by experiments.

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“…In the middleware, the MTRR interacts with the Semantic Query from the high-level services block, with the Publish/Subscription Manager component from the Data Management and with all the three reporter components from the Low-level Services block. It also makes use of the mission plan structure defined in the SWARMs information model [42]. The following subsections give a brief description of the components interacting with the MTRR and the mission plan model being used.…”

mentioning

confidence: 99%

“…A semantic ontology has been created for the SWARMs project, as described in [42]. This ontology defines a common information model that provides a non-ambiguous representation of a swarm of maritime vehicles and missions.…”

mentioning

confidence: 99%

“…It must be noted that the MTRR has been conceived to send and store information used for mission planning, execution and data retrieval and is oblivious to the ontology that is used for this purpose, due to the fact that the MTRR has been designed, implemented and tested as software capable of sending information to an ontology, regardless of which one is used. Development works strictly related to ontologies and ontologies for underwater vehicles can be tracked in [42].…”

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confidence: 99%

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Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles

Martínez¹,

Martínez-Ortega²,

Rodríguez‐Molina³

et al. 2020

Applied Sciences

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In recent years there has been an increasing interest in the use of autonomous underwater vehicles (AUVs) for ocean interventions. Typical operations imply the pre-loading of a pre-generated mission plan into the AUV before being launched. Once deployed, the AUV waits for a start command to begin the execution of the plan. An onboard mission manager is responsible for handling the events that may prevent the AUV from following the plan. This approach considers the management of the mission only at the vehicle level. However, the use of a mission-level manager in coordination with the onboard mission manager could improve the handling of exogenous events that cannot be handled fully at the vehicle level. Moreover, the use of vehicle virtualization by the mission-level manager can ease the use of older AUVs. In this paper, we propose a new mission-level manager to be run at a control station. The proposed mission manager, named Missions and Task Register and Reporter (MTRR), follows a decentralized hierarchical control pattern for self-adaptive systems, and provides a basic virtualization in regard to the AUV’s planning capabilities. The MTRR has been validated as part of the SWARMs European project. During the final trials we assessed its effectiveness and measured its performance. As a result, we have identified a strong correlation between the length of mission plan and the time required to start a mission ( ρ s = 0.79 , n = 45 , p 0.001 ). We have also identified a possible bottleneck when accessing the repositories for storing the information from the mission. Specifically, the average time for storing the received state vectors in the relational database represented only 18.50% of the average time required for doing so in the semantic repository.

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SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Cited by 31 publications

References 28 publications

RTPO: A Domain Knowledge Base for Robot Task Planning

RTPO: A Domain Knowledge Base for Robot Task Planning

High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario

Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles

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