On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model

Author:

Romero-Garcés AdriánORCID,Hidalgo-Paniagua AlejandroORCID,González-García MartínORCID,Bandera AntonioORCID

Abstract

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehouse.

Funder

Gobierno de España and FEDER funds

University of Málaga

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference30 articles.

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Graph Neural Network for Building Prediction Agents in Intent-Based Zero-Touch Networks;ICC 2024 - IEEE International Conference on Communications;2024-06-09

2. Endowing Runtime Self-adaptation to an Autonomous Pallet Truck;2023 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC);2023-04-26

3. CLARA: Building a Socially Assistive Robot to Interact with Elderly People;Designs;2022-12-13

4. Inner Speech: A Mechanism for Self-coordinating Decision Making Processes in Robotics;ROBOT2022: Fifth Iberian Robotics Conference;2022-11-19

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