## 1 Introduction
The algorithm employs orthomosaic images and neural networks for path construction. However, the study does not take the soil Terramechanics into consideration. Examples of studies using general-purpose optimization algorithms and heuristics include [13; 14; 15]. The study in [13] proposes an energy-aware shortest path optimization algorithm based on a probabilistic roadmap planning method for task-performing UGVs that visit multiple nodes on cattle farms (rough and uneven terrain). The work of [14] considers UGV shortest path planning by employing a chaotic meta-heuristic optimization method based on Q-learning with velocity estimation coupled with Terramechanics. The work of [15] employs a receding horizon path planning algorithm that fragments the main problem into several local path planning problems for UGVs involved in picking-and-delivering operations at multiple nodes. A common aspect of all the above studies is that the investigated problems are formulated as constrained optimization problems that are, in general, hard to solve to exact optimality. It is worth noting that a bulk of studies in the literature are devoted to path planning and energy management for electric race cars--see, e.g., [16; 17; 18]. These studies are, in general, concerned with finding optimal trajectories for race cars within their race tracks, often combined with the car velocity control. In particular, the work of [16] proposes a velocity planner optimization algorithm based on multi-parametric sequential quadratic optimization for autonomous electric race cars moving at very high speeds (above 200 km/h). The main inputs for the optimization problem are the energy strategy (power limit), graph-based path planner, and friction estimation. The outputs are the optimal race car velocity and trajectory along this path. Similarly, the study in [17] proposes a three-level semi-offline nonlinear optimization path planning algorithm based on optimal control for autonomous electric race cars lap time minimization. The main inputs for the optimization problem are the thermodynamic variables of the race car, physical constraints such as maximum torque and tires, mathematical constraints such as calculation time, and external constraints such as maximum velocity and obstacles. On the topic of race car lap time minimization, the work of [18] proposes an optimal energy management methodology that achieves the best possible lap time for hybrid electric race cars modeled as a convex optimal control optimization problem. The main inputs for the problem are the vehicle dynamics (mainly the vehicle's mass and propulsive and drag forces), in addition to physical, operational, and regulatory constraints (such as maximum fuel flow and consumption and turbocharger efficiency). Some other studies have focused on path planning for passenger autonomous vehicles on public roads, such as [19; 20]. The work of [19] constructs a cost-function graph model between predefined waypoints, where the road and center lines are represented using parametric cubic spline. The optimal path (lowest cost) and vehicle velocity control are chosen mainly based on static safety (road edges and static obstacles), comfortability (continuity and smoothness of a path), dynamic safety (other vehicles on the road). The study conducted in [20] discussed handling geometry, nonholonomic and dynamics constraints in a human-like and layered fashion. The constructed paths lie in the free space and take in consideration vehicle kinematics and dynamics. The proposed path planning algorithm solves for each constraints individually, starting with global path search, followed by collision checking and path selection, and ending with speed planning. It is worth noting, however, that the above problems have a different mathematical structure and constraints as compared to the UGV path planning problem of this paper.