Optimization focused on field completion times rather than total machine time.Optimization obtained by transformation into a Vehicle Routing Problem.Significant reductions possible with meta-heuristic procedures like Tabu Search.Tabu Search is more important in complex fields and with more vehicles. The Vehicle Routing Problem (VRP) is a powerful tool used to express many logistics problems, yet unlike other vehicle routing challenges, agricultural field work consists of machine paths that completely cover a field. In this work, the allocation and ordering of field paths among a number of available machines has been transformed into a VRP that enables optimization of completion time for the entire field. A basic heuristic algorithm (a modified form of the common Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed for optimization. Both techniques were evaluated through computer simulations in two fields: a hypothetical basic rectangular field and a more complex, real-world field. Field completion times and effective field capacity were calculated for cases when 1, 2, 3, 5, and 10 vehicles were used simultaneously. Although the Tabu Search method required more than two hours to produce its solution on an Intel i7 processor compared to less than one second for the method based on Clarke-Wright, Tabu Search provided better solutions that resulted in reduced field completion times and increased effective field capacity. The benefit provided by Tabu Search was larger in the more complex field and as the number of vehicles increased. With ten vehicles in the real-world field, the benefit provided by Tabu Search over the modified Clarke-Wright resulted in reduced completion time of 32%, but even with only three vehicles a 15% reduction was obtained. While ten vehicles may only be applicable with future autonomous machines, simultaneous usage of three machines is not uncommon in current production. As producers consider using multiple machines to improve field completion times and effective field capacity, optimization of the vehicle routing will play an important role in ensuring those improvements are fully realized.

This study tested an ultrasonic sensor's ability to detect several objects commonly encountered in outdoor agricultural or construction environments: a water jug, a sheet of oriented strand board (OSB), a metalfence post, a human model, a wooden fence post, a Dracaena plant, a juniper plant, and a dog model. Tests were performed with each target object at distances from 0.01 to 3 m. Five tests were performed with each object at each location, and the sensor's ability to detect the object during each test was categorized as "undetected," "intermittent," "incorrect distance," or "good." Rigid objects that presented a larger surface area to the sensor, such as the water jug and OSB, were better detected than objects with a softer surface texture, which were occasionally not detected as the distance approached 3 m. Objects with extremely soft surface texture, such as the dog model, could be undetected at almost any distance from the sensor. The results of this testing should help designers offuture systems for outdoor environments, as the target objects tested can be found in nearly any agricultural or construction environment.