# Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications
## 3 Analysis of Knowledge Representation Systems
### Reasoning Scope
#### 3.6.3 Task Execution and Action Planning  
Cognitive capabilities in a robot enable it to interpret task execution and actions. For this, a proper representation of the actions to perform specific tasks by the robot in the environment is essential, which has been included in most of the compared KR systems. KnowRob [68] uses motion control reasoning to provide tight coupling of action representation and executable motion descriptions for attaining competence in a manipulation task. It performs simulation-based reasoning to envisage the results of motion parametrization and plans for predicting the precise course of action. It avoids the frame problem, and compared to the logic like reasoning, it also has the advantage of capturing physical behavior. Besides, its knowledge representation about collisions and gravity is more generic than rules or manual knowledge. OROSU [69] is designed to track action execution during orthopedic surgery. OROSU [69] uses HermiT and Pellet reasoners to perform reasoning on the actions in the real scenario for performing surgical procedures, e.g., hip surgery. It uses Pellet to obtain the query output of the defined action, e.g., \(obtain3 DfemurModelFromUS\) and \(RoboticBoneTracking\) for performing the tasks in the \(OperatingRoom\). It accomplishes more complex queries using the HermiT reasoner, e.g., to obtain a 3D model from US images or robotic bone tracking to perform the task in the operating room. CARESSES [70] enables the robot to match its behavior with the cultural identity of the user, which helps the robot to form its plan and execute the actions in a culturally aware manner for making appropriate decisions to achieve the goal. It uses constraint-based approaches [109] for plan generation with a focus on cultural constraints and human-aware planning [110]. In the same direction, Reference [111] used a constraint-based planner with cultural sensitivity [112] to execute the robot's actions and maintain the states of the environment and people. PMK [71] uses a fast-forward task-planning [95] approach combined with physics-based motion planning [96] in the TAMP module, which facilitates task planning and execution by providing a sequence of actions to the robot. PMK uses geometric reasoning, dynamic interactions, along with manipulation constraints to support this process. Its execution module performs the serving task. SARbot [72] uses the JESS reasoner, which is a Java-based CLIPS reasoner [113]. It first reasons about the task according to SWRL rules and actual knowledge, then it performs the task by breaking it into automatic actions. It allows the robot to find the victim's location through inference and semantic information, while task reasoning is performed with JESS. IEQ [73] enables the robot to execute human well-being tasks for visual, thermal, and acoustic comfort in the indoor environment. The Light_Sound data acquisition module obtains and sends user's responses to the normative reasoner. The normative reasoner performs compliance evaluation with normative standards. Consequently, the robot gives suggestions to the user based on the compliance results and user's perceptions and preferences to solve possible discomfort in the indoor environment. The reasoning module in SmartRules [74] executes actions based on contextual information. It allows reactive reasoning based on the closed-world assumption (CWA) and starts the reasoning process by converting SmartRules into the Drools [114] inference engine. It uses RRC to process information from the abstraction layer and generates actions after the reasoning process. Then, the platform decodes these actions into actual commands that are sent to the actuators. In the context of a person's presence, but not near a display MO, an action is triggered by RRC to move the service robot to the person's location.