# Mobile Robotics in Agricultural Operations:
## 3 Review
The study findings demonstrated reductions in working time (10.7-32.4%), in non-working distance (17.5-40.2%), and in total travelled distance (2.2-6.4%). Moreover, the authors [44] developed and simulated a path planning algorithm for the autonomous navigation of a mechanically and electrically modified tractor to minimize fuel consumption and related atmospheric emissions while performing a number of agricultural processes (i.e., weed and pest control). The algorithmic solution simultaneously considered a range of parameters including UGV's fuel consumption, engine emissions, obstacle detection, weed identification, optimal turning angle for the UGV, and paths for area coverage. [PERSON] et al. (2016) [45] implemented a simulated annealing-based algorithm for area coverage in herbicide applications. To validate the proposed algorithm, which facilitates both global and local planning, five case studies were investigated focusing on the minimization of: (i) the distance travelled in the crop headlands using different turning radii; (ii) the input cost and required time for a fleet of homogeneous vehicles; (iii) the input cost for the required time and both criteria for a fleet of both homogeneous; and (iv) heterogeneous vehicles with small tanks; and (v) the time for a fleet of homogeneous UGVs in a field divided into three parcels. Finally, the work presented in [46] developed a multi-objective path planner for a mobile robot in greenhouse environments. The developed algorithm considered travelling distance and routing angel subject to pesticide tank capacity constraints, and it was tested against four alternative routing test cases. Considering UAVs, the authors of [47] proposed an area coverage planning system for ensuring a UAV's energy efficiency and high-resolution aggregated images. The structure of the system was based on into three distinct algorithms for: (i) estimating the consumed energy related to the UAV's operational speed, acceleration, flying maneuvers and height; (ii) determining an energy efficient path to cover the entire field; and (iii) detecting fail-safes based on the current status of the UAV (i.e., battery level, GPS and ground station signals) to ensure the completion of the operations or the safe return of the UAV to the home position. In addition, the authors in [48] presented a two-level algorithmic approach for the path planning problem of UAVs for spraying operations. Specifically, the first level refers to a grid point-based method for decomposing a field and identifying navigation points, whereas the second level considers a modified boustrophedon algorithm used in coverage path planning field. The integrated approach renders path planning flexible for complex fields, and it is useful for multi-agent crop dusting. The authors in [49] presented an off-line area coverage planning approach where the area to be scanned is decomposed into a finite set of cells for the optimal sequencing of these cells under the criterion of minimizing the number of changes in the direction of UAVs. To that end, an adaptedmetaheuristic algorithm was simulated and provided individual paths for multi-UAVs or equivalently a number of paths for a single UAV. Finally, in the work [50] presented a seminal work on complete mission execution that involves planning and control of the UAVs in actual field experiments. Except for area coverage planning, the study concurrently investigated the problems of task generation (area partitioning) and task assignment (sub-areas' allocation) to minimize the number of turns and overlapped areas which are both affecting the total coverage time. The approach took into account the characteristics of different UAVs and thus it can address problems of heterogeneous fleets. The authors in [51] proposed a two stage kino-dynamic planning algorithm for cost-based navigation which was tested in experimental weeding applications by using a non-holonomic vehicle.