# Mobile Robotics in Agricultural Operations:
## 3 Review  
In order to address large-scale motion planning problems, studies often apply a multi-layered analysis framework mainly consisting of [38]: (i) a global planning layer that produces an optimal path with a priory knowledge of the general environment and static obstacles - this layer divides the workspace into smaller areas; and (ii) a local planning layer that guarantees safety in uncertain environments--this layer recalculates a safe path whilst avoiding dynamic obstacles and changes in the workspace. We follow the same analysis framework here on the review presentation flow, first presenting articles that deal with global planning approaches and then articles dealing with local planning approaches. The literature on global planning considers both UGVs and UAVs. Selected studies are analyzed below. With regard to UGVs, the work that presented in [39], a path planning approach for car-like mobile robots in orchard operations based on the A* algorithm and Manhattan metrics (grid map). The proposed approach uses a configuration space that utilizes restrictions, capturing safety and agri-technical limitations, via implementing penalty functions that consider the steering angle, travel direction, roll and pitch angles, and region traversability. In addition, the authors in [40] developed a two-stage randomized kino-dynamic motion planning approach for non-holonomic vehicles (Ackerman steering). At the first stage, a relaxed two-dimensional (2D) path planning was executed using a Rapidly-exploring Random Trees algorithm. A metric function combining path traversal time, heading change, and failure rate was used as a cost function to select the nearest neighboring node. At the second stage, the trajectory of the vehicle was plotted using B-splines for enabling the motion planner to plot smooth paths while ensuring geometric continuity and satisfying maximum curvature conditions. Authors in [41] examined pasture fields in dairy farms, particularly focusing on the associated agronomic needs. In this context, pasture fields are often divided in three separate operational-wise problems, i.e., reseeding, weeding, and fertilizing by redistributing cowpats. Therefore, [41] defined  
Figure 4: Planning attributes and corresponding instances in locomotion planning for agricultural robots. sub-regions and implemented the Travelling Salesman Problem approach to find the shortest path for a mobile robot visiting these areas. In addition to the topological problem, the proposed system also took into account the feasible trajectory kinematic and dynamic constraints (e.g., working width, turning capacity). In addition, the authors in [42] deployed a set of algorithms for selective patch treatments in a field with row crops by using a robotic vehicle. A mixed weighted networking graph represented the system, which was solved as the asymmetric travelling salesman problem using the parthenogenetic algorithm. The ability of the robot to move in both directions is factored in the mixed graph and the parthenogenetic algorithm leverages this technical feature to generate an optimized path by choosing to reverse even in mid-row operations to reach the next node. Additionally, the work presented in [21] tested a path planning algorithm along with a user interface for autonomous field coverage in three case studies: (i) coverage of three separate fields; (ii) coverage of three neighboring fields in one mission; and (iii) coverage of a single field and simulating a spraying operation with two refilling locations. The user interface incorporates the field's geomorphological information along with the UGV's properties to calculate the desired path. An algorithmic approach of the vehicle routing problem was followed to produce the admissible path and to reduce the working time. The authors in [43] also developed and tested, both via simulation and real-world experiments, a route planning approach for an orchard's spraying operations based on B-Patters algorithm.