## 2 Related Work
### Competitions
#### 2.1.2 RoboCup Logistics League  
The RoboCup Logistics League (RCLL) focuses on a logistic scenario in a smart factory environment. The environment is partially surrounded by walls, inside which seven machines for each team are randomly distributed. The available machines are of four types, and each is responsible for a specific processing step. The competing robots (three for each team) have the task of transferring objects between processing stations to assemble a final product. The robots have a Robotino mobile base [16] on which an extension tower with a platform is mounted. The tower has a specific height that allows an attached gripper to reach the conveyor belt inputs and outputs of all machines [7; 17].  
From the task planning point of view, the most demanding part of the competition is the _production phase_. The robots can execute mainly three types of physical actions: navigating, loading, and unloading, but the challenge is to determine their optimal sequence to carry out the given orders. For this purpose, different scheduling and task planning approaches are deployed. The more complex approaches are integrated in a multi-layer planning-execution architecture, while the more rigid approaches are directly connected to the lower-level hardware. For example, the GRIPS team [18] used a hierarchical task network (HTN) approach for the 2020 competition, in which the main goals are decomposed to basic actions. Then, a scheduler sends the generated sub-tasks according to some priority rules to the idle robots. The Carologistics team [19] used automated planners in the 2018 competition, integrated into the CLIPS executor [20] to plan the actions required to reach the sub-goals determined in a previous step from the received orders. Other participants, such as the robOTTO team [21], have used an optimizer to generate sub-tasks that are implemented on the lower level with FSMs. The entire planning and execution process was also implemented only through FSMs, for example, by the ER-Force team [22] and the Solidus team [23] in the 2018 competition.  
An interesting point of view is presented in [24], where it is argued that the most successful teams in RCLL competitions in the last few years, Carologistics [19] and GRIPS [18], used quite simple approaches that focused more on task scheduling, than on task planning. The authors also suggest a series of measures to foster the planning aspects. One such measure could be to remove the hard requirements to process a specific piece only at a specific location. Another option could be to allow the robots of the team to carry more items at a time or just to scale up the problem by adding more robots, more objects, or more stations.  
Another competition that uses a domain similar to the one used in RCLL competitions is the _Planning and Execution Competition for Logistics Robots in Simulation_[25]. In the 2018 edition, only three teams participated, all of them using automated planners for organizing the actions for the competing robots [26]. Two teams used ASP-based planners [27], while the third used the temporal planner POPF [28], with the ROSPlan framework [29].