# Mobile Robotics in Agricultural Operations:
## 4 Discussion  
### Locomotion Planning as Part of the Overall Mission Planning  
Mission planning is the process of determining the sub-tasks that need to be executed (i.e., task decomposition), assigning a sequence for these sub-tasks' execution, in the case of a single-robot, or assign individual autonomous robots to these sub-tasks, in the case of multiple robots, and determining a corresponding course of collision-free actions (i.e., locomotion planning) [50] (Figure 5). In principle, task decomposition refers to the process of partitioning a task into a number of well-defined and non-overlapping sub-tasks. For example, in terms of locomotion in agricultural mobile robotics, explicitly and implicitly connected tasks are identified. Considering that explicitly connected tasks mainly regard area coverage operations, the decomposition of an area to be covered in sub-regions is a task-partitioning process [57; 58]. In this case, each individual sub-field area is considered as a task for a single mobile robotic unit. By contrast, the sequence for visiting a series of waypoints or entities in a specified field (or sub-field area) implicitly connects to locomotion tasks, including, for example, the case of generating and traversing fieldwork tracks while ensuring full area coverage regardless of any terrain geomorphological characteristics or a robot's driving specifications [59].  
In the context of multiple-robot systems, knowledge about the level of collaboration among the utilized mobile robots is required to enable task sub-division and allocation based on performance criteria, availability constraints, etc. Thereafter, task decomposition and allocation can inform the locomotion planner of each individual vehicle [50]. In the case of collaborative mobile robots, the actions of an individual unit depend on the activities of the participating fleet members; therefore, the performance of a robotic system is evaluated at an aggregated fleet level [60]. In cases where no collaborations underpin a multi-robot system, the tasks are assigned either when an operation commences or in discrete points in time during task execution. The majority of real-world cases in automated agriculture correspond to the second case.  
Regarding field operations, there is a lack of works dealing with a complete mission planning system. Just to mention a few works dealing with mission planning, the authors of [61] presented an XML-based mission planner for an autonomous agricultural tractor for field coverage following a tree hierarchy model comprising of several actions including: (i) interpretation of field geometry, dimensions, sub-regions and critical points; (ii) determination of the type of motion between successive points along with the sequence of points to follow; and (iii) deliberation of operations in every point in the field. In [43], an approach for the real-time path planning of a fleet of tractors in peat moss harvesting was provided. Each autonomous tractor could perform four tasks: drive to field; harvest the field; drive to a pile; and dump at a pile. To provide planning for these tasks two methods were applied, a visibility graph planner (utilizing polygonal features) and a grid-based planner (2D cells of \(1\times 1\) m). To ensure the collision-free navigation, all the pre-planned tasks were executed in a GPS-based mapped area.  
Figure 5: The place of locomotion planning in overall mission planning.