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The paper presents an implementation of knowledge representation and task representation, based on ontologies for an Industrial Robotic Application. The industrial application is to insert up to 56 small pins, e.g., sealants, in a harness box terminal for the automotive industry. The number of sealants and their insertion pattern vary significantly with the production requests. Based on the knowledge representation of the robot and also based on the tasks to be performed, plans are built and then sent to the robot controller based on the seal pattern production order. Moreover, the robotic system is capable to perform re-planning when an insertion error is reported by a machine vision system. The ontology-based approach was used to define the robot, the machine vision system, and the tasks that were needed to be performed by the robotic system. The robotic system was validated experimentally by showing its capability to correct seal insertion errors, while re-planning.

The evolution of production systems has established major challenges in internal logistics. In order to overcome these challenges, new automation solutions have been developed and implemented. This paper is a literature review and analysis of selected scientific studies, which has as the main focus the existing solutions in robotics for internal logistics. The review aims to provide a broad perspective of the existing robotic systems for internal logistics to determine which research paths have been followed to date and highlight the current and future research directions. The survey has been subdivided into the following topics: localisation and path planning; task planning; optimisation and knowledge representation in robotic systems; and applications. The analysis of the works developed until the date of this review highlights the appearance of strategies in the different disciplines based on meta-heuristics. These are replacing the classical and heuristic approaches due to their limitations in dealing with a large amount of information in internal logistic systems. Due to the increase of information that robotic agents have to process, strategies based on semantic knowledge have been gaining prominence to make the domain knowledge explicit and eliminate ambiguities, allowing agents to reason and facilitate knowledge sharing between robotic agents and humans.

Autonomous mobile robotic agents are increasingly present in highly dynamic environments, thus making the planning and execution of their tasks challenging. Task planning is vital in directing the actions of a robotic agent in domains where a causal chain could lock the agent into a dead-end state. This paper proposes a framework that integrates a domain ontology (home environment ontology) with a task planner (ROSPlan) to translate the objectives coming from a given agent (robot or human) into executable actions by a robotic agent.