# Mobile Robotics in Agricultural Operations:
## 3 Review
In the first stage, the algorithm explored all the feasible trajectories from an initial to an end-point via using the Rapidly exploring Random Trees as a probabilistic path planner. In the second stage, the movement cost at a non-collision path assignment was minimized via deploying a gradient descent algorithm. Furthermore, in the [52] tested a simulated annealing and a genetic algorithm for the kino-dynamic planning for autonomous agricultural vehicles within field areas with obstacles and/or complicated field borders. The study results demonstrated that the annealing algorithm outperforms the genetic in all tested cases. In [53], a path planning approach was developed by combining neural networks for describing the motion of the agricultural mobile robot and a genetic algorithm for optimization. The optimization objective was the minimization of the total changes in the steering angle and the required travel time. Finally, multiple-vehicle systems imply the use and physical interaction among the systems within a fleet of mobile robots [54]. The notion of \"physical\" interaction is used to distinguish multiple robotic systems from multiple-agent systems which usually refer to distributed computer systems involving individual stationary nodes. Notably, in multi-robot systems, the locomotion plan is complex owing to the multi-aspect requirements for avoiding: (i) collision with obstacles; (ii) deadlocks; (iii) collision with other robots; and (iv) traffic congestion situations. This generates the notion of coordination that is the generation of distributed strategies for a fleet of cooperating robots in a given task to optimize the performance. The present research recognizes coordination as a control discipline rather than a planning one. However, due to the importance of coordination in the execution of multi-robot enabled planned tasks, and due to the inherent optimization process in this kind of control, it was considered valuable to include aspects on motion coordination in this study. Indicatively, the authors of [55] tested, at both simulation and experimental levels, a framework that utilized the A* algorithm with the Euclidian distance to plan a point-to-point collision-free navigation of multiple UGVs. The dynamic structure of the fleet allowed potential de-formations to avoid obstacles and execute complicate kinematics. Additionally, the authors of [56] presented a distributed control framework for the coordination of the motions of field robotic teams which supports both cases of master-slave and peer-to-peer operation modes. The results showed that under this framework field robots can coordinate with nearby team members by altering their trajectories (in terms of velocity profiles or path shapes) and avoid collisions. Table 1 taxonomizes the selected studies on locomotion planning according to the defined planning attributes and also provides information about the application features of each approach. It is evident that the defined attributes apply to all of the approaches. Furthermore, the variation of the instances of these attributes among the various approaches maps the current state of research in the domain of mobile robots in agriculture. For example, there is clear preference toward global planning approaches compared to local planning approaches. The same stands also between deliberative and reactive reasoning architectures in favor of the former. Finally, although agricultural environments are characterized in their majority by rough terrain, there is a lack in approaches dealing with 3D world models. All of the above reveal gaps in the current research.