**1. Introduction**

Up until present, the application of scientific and technological developments through increased mechanization and precision farming have provided several opportunities in agricultural production and within forage handling operations. Some promising engineering developments in the 20th century with regard to forage handling include forage harvesters, balers, and the automated wrapping equipment of balers using stretch films 25 μm thick to reduce the risks of dust, molds, spores, and mycotoxin respiratory allergenic disorders in livestock and humans. Baler machines have made it possible to trade silage (harvest and storage of moist grass using fermentation) in portable packages between farms, which typically weigh 600–800 kg freshly cut per bales and are more popular on smaller farms with limited labor and financial resources to construct silos [1,2].

Bales made up of hay or silage formed by hay are usually too heavy to be picked up by humans alone. Thus, they are picked up from fields using conventional utility vehicles such as tractors or loaders operated by a human. These kinds of operations are labor intensive and associated with health and accident risks [3]. There is also a potential to further improve the efficiency and environmental impact since most decisions are made by humans and thus limited to human capacities in terms of sensing, multitasking, planning, consequence analysis, etc.

Therefore, in this study, the possibility of using a new autonomous agricultural vehicle with the neighborhood pick-up capabilities concept (AVN) was investigated. The research focused on off-board path planning, which is a critical task within the complete automation process of the bale pick-up operation.

Research in the route or path planning of agricultural field tasks can be broadly categorized into two groups based on the similarity of operations: coverage path planning (CCP) and point-to-point path planning (P2P). It has been observed by [4] that agricultural

**Citation:** Latif, S.; Lindbäck, T.; Karlberg, M.; Wallsten, J. Bale Collection Path Planning Using an Autonomous Vehicle with Neighborhood Collection Capabilities. *Agriculture* **2022**, *12*, 1977. https://doi.org/10.3390/ agriculture12121977

Academic Editors: Jin Yuan, Wei Ji and Qingchun Feng

Received: 4 October 2022 Accepted: 14 November 2022 Published: 22 November 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

operations that required coverage path planning have been slightly more investigated. Most solutions for the path planning of agricultural field operations are based on optimization methods utilizing heuristic approaches or metaheuristic approaches depending upon the size and context of the problem [5]. In situations where vehicle routes must be planned over large areas with high economical risk, methods such as metaheuristics perform an extensive search for a solution and should thus be preferred [6].

Route planning for agricultural field operations (AFOs) involving the use of vehicles is referred to as vehicle route planning (VRP), which is a well-studied problem in the field of operational planning. Recently, VRP solutions have been applied to the planning and execution of various agricultural field tasks by researchers for the scheduling of the transportation of livestock [7,8] mission planning for coverage operation such as grass mowing and seedling [9], biomass operation scheduling [10], farm-to-farm path determination for scheduling crop harvesting [11], and route planning for fertilizer application [12]. Recently, a decision tool to support farmers in the operational planning of field operations was proposed by [13] to assist in field partitioning, route generation, and evaluation.

Significant improvements have been shown for AFOs in research by the automation of the AFOs. A study [14] on field coverage operations for an autonomous tractor using a mission planner showed a 50% reduction in non-working distance. Coverage operations were then further studied for irregular shaped fields with obstacles [15,16]. In another implementation by [17], the optimal covering route and feasible positions for grain transfer between the combine harvesters and tractors were generated using VRP and the minimum cost network flow.

The application and comparability of metaheuristics for AFOs have been widely studied and is still ongoing. Recently, a hybrid genetic algorithm (GA) was tested by [18] for a capacitive vehicle route problem (CVRP) by utilizing Gillett and Miller, Downhill, and nearest neighbor heuristics to generate the initial population and refine solutions of GA. Experimental results showed that the hybrid approach generated good solutions for CVRP with low computational cost. In another research by [19] with regard to capacitated coverage path planning problem for arable field, two popular metaheuristics—simulated annealing optimization (SAO) and ant colony optimization (ACO) techniques—were evaluated and it was found that SAO performed better than ACO. Aside from AFOs, a multi-objective optimal solution to priority-based waste collection and transportation was proposed by [20] using particle swarm optimization, local search, and simulated annealing (SAO). The optimized solution resulted in a 42.3% reduction in the negative effects of greenhouse gas emissions compared to traditional waste management.

So far, few studies have investigated the bale management in fields. There exists few published studies on the sequence optimization of the bale collection operation using wagons or loaders. The intended bale field operation was described as a bale collection problem (BCP) and was solved as a traveling salesman problem using GA by [21]. While in another study on BCP in [22], a heuristic-based approach based on K-mean clustering and nearest neighbor techniques to optimize the bale collection route were tested in simulation. Comparative results from both studies showed significant improvement in the final generated route. However, no other research studies were found on the route optimization of bale collection and no single study was found on the bale collection on fields, especially with the prerequisite of neighborhood pick-up possibilities.

#### *1.1. Objective*

The objective of the research presented in this paper was to optimize the bale collection operation by means of travelled distance using notion of an autonomous articulated vehicle with neighborhood collection capability (AVN) and compare that with traditional collection methods.

#### *1.2. Scope*

The research focused on the development of a global route plan for bale collection operations in simulation for notion of using AVN. For a global route plan, a static and known environment was considered since bale positions and fields are static entities. Bale positions were assumed to been known from a previous baling operation.

The following additional general assumptions were made:


#### **2. Research Methodology**

To investigate the effects of different bale collection strategies, a simulation approach was chosen. Path planning is typically performed in computer environments, which further makes feasibility evaluation easy compared to real life experimental strategies (i.e., to measure the feasibility on path suggestions on an actual field).

Two different approaches were studied and verified through the testing of situations with outcome pre-knowledge. The first approach imitates the bale collection strategy of farmers by always choosing the closest bales from the current position. The other approach instead uses a GA to optimize the collection order and position within a radius from which the AVN can reach. To investigate the differences in travelled distance (i.e., chosen feasibility) between a traditional and proposed collection approach, two different fields of the same size and with the same number of bales with a pre-determined distribution was studied. One was a simple rectangular field (field 1) and the other was a L-shaped field with more geometrical constraints (field 2). This enables investigations of possible dependencies on field complexity. With the fields selected, some simulation parameters could be set (e.g., grid size, inflation length, number of possible pick-up positions etc.) by conducting verifying tests to find a trade-off between the computational time and accuracy. Then, the experiments were designed by choosing which parameters to vary and thus which simulations to run. To enable comparison, the results from these simulations were then compiled into tables and some paths were also visualized, enabling the analysis of collection order as well as verification on the feasibility.

The traditional approach was generated by considering how humans would operate in a typical agricultural environment for bale collection operation. Generally, a human operator would pick-up the next visible bales closest to the present location. Such a heuristic approach could be programmed by using the nearest neighbor algorithm. Through this approach, two different cases were studied: one with a traditional pick-up vehicle which always has to go to the nearest bales and another with the AVN.

In addition, an optimization approach based on commonly used GA was further developed, thus enabling a comparison to the traditional approach. Here, two different strategies for initial population generation were used to show the effects on convergence.

Verification of the simulations were conducted by running a test simulation on configurations where the results were pre-known. In addition, the results from all simulations were analyzed manually to make sure that the paths were consistent.

#### *2.1. Model Description*

In this study, a notion of an AVN (see Figure 1) with a regular forwarder crane of 10 m long was used for the modeling. For comparison, traditional agricultural vehicles (e.g., tractors or loaders) were also modeled. These traditional vehicles are typically equipped with front loaders requiring additional traveling for the loading of each bale (i.e., they cannot load bales onto themselves). This effect is excluded in the traditional vehicle models in this study, leading to underestimation of the travelled distance.

**Figure 1.** Autonomous off-road vehicle platform—Autonomous articulated vehicle with neighborhood reach capability (AVN).

The problem formulation for the bale collection operation with a crane makes it somewhat unique. The AVN can collect bales at a radius R, which makes the situation a close enough traveling salesman problem (CETSP) [23]. The CETSP is a NP hard, combinatorial problem, and some recent solutions for CETSP have been proposed based on the discrete gravitation search algorithm and self-organizing maps [24,25]. However, the vehicle can have different carrying capacities, thus leading to a close enough traveling salesman problem with a capacity constraint. In this study, the collection sequence and collection positions minimizing the total travelled distance was searched for and thus the CETSP is defined as

$$\min\_{L} \left( \Sigma\_{\prime} \; BP, \mathbf{CP} \right) \tag{1}$$

where Σ is the bale pick up sequence; CP is the desired collection position at radius R (specified by AVN reach radius) around the bale positions (BP); *minL* is a function that calculates the minimum length tour at collection positions around each bale.
