Non-working Distance Research Articles

Integrated Route-Planning System for Agricultural Robots

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs’ performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation.

Novel Route Planning Method to Improve the Operational Efficiency of Capacitated Operations. Case: Application of Organic Fertilizer

A field area coverage-planning algorithm has been developed for the optimization and simulation of capacitated field operations such as the organic fertilizer application process. The proposed model provides an optimal coverage plan, which includes the optimal sequence of the visited tracks with a designated application rate. The objective of this paper is to present a novel approach for route planning involving two simultaneous optimization criteria, non-working distance minimization and the optimization of application rates, for the capacitated field operations such as organic fertilizer application to improve the overall operational efficiency. The study and the developed algorithm have shown that it is possible to generate the optimized coverage plan based on the required defined capacity of the distributer. In this case, the capacity of the distributer is not considered a limiting factor for the farmers. To validate this new method, a shallow injection application process was considered, and the results of applying the optimization algorithm were compared with the conventional methods. The results show that the proposed method increase operational efficiency by 19.7%. Furthermore, the applicability of the proposed model in robotic application were demonstrated by way of two defined scenarios.

R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

Route Planning for Agricultural Machines with Multiple Depots: Manure Application Case Study

Capacitated field operations involve input/output material flows where there are capacity constraints in the form of a specific load that a vehicle can carry. As such, a specific normal-sized field cannot be covered in one single operation using only one load, and the vehicle needs to get serviced (i.e., refilling) from out-of-field facilities (depot). Although several algorithms have been developed to solve the routing problem of capacitated operations, these algorithms only considered one depot. The general goal of this paper is to develop a route planning tool for agricultural machines with multiple depots. The tool presented consists of two modules: the first one regards the field geometrical representation in which the field is partitioned into tracks and headland passes; the second one regards route optimization that is implemented by the metaheuristic simulated annealing (SA) algorithm. In order to validate the developed tool, a comparison between a well-known route planning approach, namely B-pattern, and the algorithm presented in this study was carried out. The results show that the proposed algorithm outperforms the B-pattern by up to 20.0% in terms of traveled nonworking distance. The applicability of the tool developed was tested in a case study with seven scenarios differing in terms of locations and number of depots. The results of this study illustrated that the location and number of depots significantly affect the total nonworking traversal distance during a field operation.

An Arable Field for Benchmarking of Metaheuristic Algorithms for Capacitated Coverage Path Planning Problems

This study specifies an agricultural field (Latitude = 56°30′0.8″ N, Longitude = 9°35′27.88″ E) and provides the absolute optimal route for covering that field. The calculated absolute optimal solution for this field can be used as the basis for benchmarking of metaheuristic algorithms used for finding the most efficient route in the field. The problem of finding the most efficient route that covers a field can be formulated as a Traveling Salesman Problem (TSP), which is an NP-hard problem. This means that the optimal solution is infeasible to calculate, except for very small fields. Therefore, a range of metaheuristic methods has been developed that provide a near-optimal solution to a TSP in a “reasonable” time. The main challenge with metaheuristic methods is that the quality of the solutions can normally not be compared to the absolute optimal solution since this “ground truth” value is unknown. Even though the selected benchmarking field requires only eight tracks, the solution space consists of more than 1.3 billion solutions. In this study, the absolute optimal solution for the capacitated coverage path planning problem was determined by calculating the non-working distance of the entire solution space and determining the solution with the shortest non-working distance. This was done for four scenarios consisting of low/high bin capacity and short/long distance between field and storage depot. For each scenario, the absolute optimal solution and its associated cost value (minimum non-working distance) were compared to the solutions of two metaheuristic algorithms; Simulated Annealing Algorithm (SAA) and Ant Colony Optimization (ACO). The benchmarking showed that neither algorithm could find the optimal solution for all scenarios, but they found near-optimal solutions, with only up to 6 pct increasing non-working distance. SAA performed better than ACO, concerning quality, stability, and execution time.

Novel Route Planning System for Machinery Selection. Case: Slurry Application

The problem of finding an optimal solution for the slurry application process is casted as a capacitated vehicle routing problem (CVRP) in which by considering the vehicle’s capacity, it is required to visit all the tracks only once to fully cover the field, as well as complying with a specified targeted application rate. A key objective in this study was to determine an optimized coverage plan in order to minimize the driving distance in the field, while at the same time allowing for varying the application rate. The coverage plan includes the optimal sequence of tracks with a specified application rate for each track. Two algorithms were developed for optimization and simulation of the slurry application cast as capacitated operations. In order to validate the proposed algorithms, a slurry application operation was recorded, and the results of the optimization algorithm were compared with the conventional non-optimized method. The comparison showed that applying the proposed new method reduces the non-working distance by 18.6% and the non-working time by 28.1%.

Fields distinguished by edges and middles visited by heterogeneous vehicles to minimize non-working distances

In rice-wheat rotation systems in China, all vehicles working in fields have to make turns because these fields are surrounded by canals on three sides. All fields can be divided into two parts – the middle and edges which include the head, tail, and two sides of the field. In general, a field’s edge is visited only by a small vehicle because it has a lighter weight and the soil does not easily become compacted due to the vehicle’s many turns and sideways cuts. In addition, workers in heavy vehicles cannot easily distinguish ridges and crops on the two sides of the fields. Therefore, heavy vehicles can only visit the middle after the edges of these fields have already been completely worked by small vehicles. A general framework including a bi-level programming model and a two-stage algorithm constructed by a tabu search is proposed to optimize the non-working distance in intra- and inter-fields for heterogeneous vehicles based on distinguishing a field’s edges and middle. In the bi-level programming model, the lower level determines the small vehicles’ routes and the middles of which fields will be visited by heavy vehicles, and the upper level determines the optimal routes for the heavy vehicles. Then, a case study is presented to verify the effectiveness and efficiency of the solution approach. The total non-working distance is reduced by 24.2%, a significant amount compared with the operational schedule on the farm. This study proposes a general framework for farmers to optimize the non-working distance for heterogeneous vehicles working in the edges and middles of fields.

Using hybrid algorithm to reduce non-working distance in intra- and inter-field logistics simultaneously for heterogeneous harvesters

Reducing the non-working distance for agricultural vehicles is beneficial for reducing the total operational costs for farmers. In this study, we optimize the non-working distance in intra- and inter-fields simultaneously for harvesters while harvesting rice. A mixed integer programming is presented for optimizing the rice harvesting problem, where a similar working time for all harvesters are considered based on a real case. Besides, in this model, two kinds of fields, namely moist and non-moist fields are considered, where the first can only be harvested by crawler harvesters while the second can be harvested by any type of harvester. This model aims to find an optimal schedule for the harvesters. A hybrid algorithm using both adaptive large neighborhood search and tabu search techniques is implemented to optimize the problem. A case study is considered to verify the performance of the hybrid algorithm. The results indicate that the hybrid algorithm is an effective method for solving the rice harvesting problem because the total non-working distance could be improved significantly by 15.3%. The proportion of moist fields is analyzed because it influences the schedule significantly. When compared to the traditional method for optimizing the non-working distance for harvesters, the total non-working distance in this method could be reduced by 6.2%. This paper provides farmers with a general framework for reducing the non-working distance in intra- and inter-field logistics, which results in a more efficient performance of the harvesters in the fields.

Reducing agricultural fuel consumption by minimizing inefficiencies

Abstract Fuel consumption by agricultural vehicles presents a financial burden on operators, and an environmental burden on the community – so any reduction of fuel usage is of interest to all. Dividing fuel usage into functional components – working and non-working – allows prioritization of optimization efforts that also considers multiple vehicle types, capacities and fuel consumption rates. A general framework, including the mathematical model and solution approach, is proposed to optimize the fuel consumption for the non-working distance and is implemented by vehicles in intra- and inter-fields. A mixed integer model is formulated and combined with a search algorithm to find an optimal solution. Subsequently, a case study related to wheat harvesting is presented to verify the effectiveness and efficiency of the search. Compared with the operational schedule, fuel consumed by harvesters in non-working activities is reduced significantly by 19.5%. Furthermore, total fuel consumed by harvesters is reduced by 16.3%. This paper provides a general framework for reducing the fuel consumption of agricultural vehicles and makes these agricultural vehicles more environmentally friendly.

Optimisation of agricultural routing planning in field logistics with Evolutionary Hybrid Neighbourhood Search

The optimisation of the agricultural process has gained importance over the past years as a means of increasing harvest yield, reducing cost and time required to maintain and harvest the fields, and maintaining economic and environmental sustainability. This research focuses on agricultural routing planning (ARP) for farmers' fields. The objective is to minimise the intra-field distance of the agricultural machine(s) when traversing all tracks, using an Evolutionary Hybrid Neighbourhood Search (EHNS) to solve different scenario problems. To obtain datasets for the agricultural routing problem, we gathered data from previous publications describing different fields. A mathematical model representing the optimisation of these datasets is also provided. The experimental results conclude that EHNS can either out-perform or obtain the same best solution as other algorithms in the literature. Among 9 problem sets, this study could find for 56% of the cases an improved combination of tracks saving an average of 10.68% non-working distance compared to other algorithms. The results also show that EHNS successfully gets the best objective function and the fastest convergence speed compared with the published algorithms.

Route planning evaluation of a prototype optimised infield route planner for neutral material flow agricultural operations

The need to decrease unit production costs has led agricultural industries to develop larger and consequently heavier machinery. While this has increased the productivity of single machines, it has also caused significant soil compaction, which may cause reduced crop yield and crop quality. Therefore, mechanisation solutions that have both lower unit costs and reduce the risk of soil compaction are needed. Optimising infield routes will reduce labour costs, fuel consumption and field trafficking intensity, providing important benefits for infield operations. In this paper, a prototype of an optimised infield route planning tool for neutral material flow operations is evaluated. The evaluation parameters focused on distance and traffic intensity reductions, comparing the routes proposed by the tool prototype and the routes followed by a professional operator during mowing operations. The tool requires some minimum inputs: field boundaries, field gates, working width and minimum turning radius, in order to provide an optimised route. Twelve fields were recorded by a Global Positioning System (GPS) during mowing operations and later compared with the routes proposed by the tool. In all fields, the operator's normal route was longer in distance than the route proposed by the tool, being up to 18.4% longer. In total, 9.2 km of infield distance was saved, i.e. 7.5%. The traffic intensity was reduced in all fields, except for two of the smallest fields, where it equalled that of the normal route. Specifically, the traffic intensity was reduced in the working areas, as the tool confined all non-working distance to the headlands.

Quantifying the benefits of alternative fieldwork patterns in a potato cultivation system

The fieldwork pattern, which is the ordered traversal sequence of in-field tracks by the machine, plays an important role in operational efficiency. A sub-optimal fieldwork pattern is a main reason for lost time in fieldwork operations due to excessive non-working distance and time. Even though algorithms exist that can calculate an optimal route plan of a specific operation that covers the field with minimum non-working distance and time, so far no commercial navigation-aiding system for agricultural vehicles exist that can implement this type of patterns. Yet, the implementation of applicable standard fieldwork pattern in real life operations can provide near-optimal solutions compared to the simple fieldwork patterns generally selected by operators.In this paper, the potential savings from following an alternative fieldwork pattern instead of the one selected by an experienced operator are assessed. We simulate the distance and time corresponding to five selected common fieldwork patterns and compare them with the operators’ selected fieldwork pattern. In order to simulate the fieldwork patterns, four standard turn models, which are formulas for calculating the turning distance of various types of turns, are fitted to actual turns recorded in field experiments, and the turn models together with a model of in-field transport and material reloading are evaluated. As a case study three sequential operations of potato cultivation (bed forming, stone separation and planting) were recorded and analysed in three different fields. The simulation results showed significant potential savings of both distance and time in all three operations and all three case study fields by selecting the best fieldwork pattern instead of the actual selected pattern: For bed forming the savings were up to 18.4% in distance and 32.7% in time; For stone separation the savings of distance and time were up to 35.0% and 60.9%; For the planting the savings were up to 22.6% in distance and 24.8% in time. The increase in time-based field efficiency was up to 2.7%, 7.2% and 7.1% for bed forming, stone separation and planting, respectively.

Route planning for orchard operations

Mission and route planning for an agricultural robot.Orchards is a well-suited operational environment for the application of deterministic behaviour robotic systems.Modelling of inter- and intra-row orchards operations.Reduction in the non-working time ranged between 10.7% and 32.4%. Orchard operations are considered a promising area for the implementation of robotic systems because of the inherent structured operational environment that arises from time-independent spatial tree configurations. In this paper, a route planning approach is developed and tested using a deterministic behaviour robot (named AMS - autonomous mechanisation system). The core of the planning method is the generation of routing plans for intra- and inter-row orchard operations, based on the adaptation of an optimal area coverage method developed for arable farming operations (B-patterns). Experiments have verified that operational efficiencies can be improved significantly compared with the conventional, non-optimised method of executing orchard operations. Specifically, the experimental results showed that the non-working time reduction ranged between 10.7% and 32.4% and that the reduction in the non-working distance ranged between 17.5% and 40.2% resulting to savings in the total travelled distance ranged between 2.2% and 6.4%.

Operations planning for agricultural harvesters using ant colony optimization

An approach based on ant colony optimization for the generation for optimal field coverage plans for the harvesting operations using the optimal track sequence principle B-patterns was presented. The case where the harvester unloads to a stationary facility located out of the field area, or in the field boundary, was examined. In this operation type there are capacity constraints to the load that a primary unit, or a harvester in this specific case, can carry and consequently, it is not able to complete the task of harvesting a field area and therefore it has to leave the field area, to unload, and return to continue the task one or more times. Results from comparing the optimal plans with conventional plans generated by operators show reductions in the in-field nonworking distance in the range of 19.3-42.1% while the savings in the total non-working distance were in the range of 18-43.8%. These savings provide a high potential for the implementation of the ant colony optimization approach for the case of harvesting operations that are not supported by transport carts for the out-of-the-field removal of the crops, a practice case that is normally followed in developing countries, due to lack of resources.

Benefits from optimal route planning based on B-patterns

B-patterns are algorithmically-computed sequences of field-work tracks for agricultural vehicles completely covering an area that do not follow any pre-determined standard motif, as in the conventional field-work patterns, but are a result of an optimisation process under one or more selected criteria. Here, an initial approach for the assessment of the benefits derived from the implementation of B-patterns is presented. This approach is based on the comparison of B-patterns against conventional field work patters, by simulating the operational performance of the different patterns on template fields for different combinations of field and machinery characteristics. The assessment of the benefits of B-patterns showed reductions in non-working distance up to 58.65% and increases in the area capacity, i.e. the processed area per time unit, were up to 19.23% when compared with different types of conventional field-work patterns.

Improving the efficiency of spatially selective operations for agricultural robotics in cropping field

Cropping fields often have well-defined poor-performing patches due to spatial and temporal variability. In an attempt to increase crop performance on poor patches, spatially selective field operations may be performed by agricultural robotics to apply additional inputs with targeted requirements. This paper addresses the route planning problem for an agricultural robot that has to treat some poor-patches in a field with row crops, with respect to the minimization of the total non-working distance travelled during headland turnings and in-field travel distance. The traversal of patches in the field is expressed as the traversal of a mixed weighted graph, and then the problem of finding an optimal patch sequence is formulated as an asymmetric traveling salesman problem and solved by the partheno-genetic algorithm. The proposed method is applied on a cropping field located in Northwestern China. Research results show that by using optimum patch sequences, the total non-working distance travelled during headland turnings and in-field travel distance can be reduced. But the savings on the non-working distance inside the field interior depend on the size and location of patches in the field, and the introduction of agricultural robotics is beneficial to increase field efficiency.

Driving Angle and Track Sequence Optimization for Operational Path Planning Using Genetic Algorithms

The objective of this article was to develop an initial approach for a method to combine two recently developed methods related to the field area coverage problem. The first stage generated a field geometrical representation and the second stage aimed to optimize the routing of agricultural vehicles within this geometrically defined world. In the first stage, using the former method the optimal driving direction is derived based on the minimization of the overlapped area. The second stage uses the later method, the optimal routing is determined for the derived driving direction and is based on the minimization of the non-working distance. In its current state, the developed approach can provide optimal solutions in terms of overlapped area and sub-optimal solutions in terms of total non-working travelled distance. Still, these sub-optimal solutions proved more efficient compared to the conventional field work patterns. Improving this sub-optimality as well as the reducing the computation times of the method in order to be feasible for real-time implementation, are considered issues of future researching.

A Mission Planner for an Autonomous Tractor

In this article, a mission planner of field coverage operations for an autonomous agricultural tractor is presented. Missions for a particular autonomous tractor are defined using an XML (extendible markup language) formatted file that can be uploaded to the tractor through the user interface. Using the tree hierarchy of the mission file, several actions are determined, including the sequence of points the tractor has to follow, the type of motion between successive points (e.g., straight motion or maneuvering), the type of predefined turning routine used in maneuvering, and the actions that should be taken once the tractor reaches the desired point (e.g., raising or lowering the attached tool, turning on or turning off the power take-off). In order to automatically create the XML mission files, a program was developed using the MATLAB technical programming language. The program uses data regarding the field (geometry, dimensions, field sub-regions, working direction, initial and final desired locations of the tractor), the operating width, and the operation type (mowing, spraying) as inputs. The planning method is based on an algorithmic approach where field coverage planning is transformed and formulated, via semantic representations, as a vehicle routing problem (VRP). By using this approach, the total non-working distance can be reduced by up to 50% compared to the conventional non-optimized method. Three sets of experiments are presented. In the first set, three fields were separately covered; in the second set, three neighboring fields were covered as part of a single tractor mission; and in the third set of experiments, a single field was covered during a hypothetical spraying operation for two different locations of the refilling facility.

Minimising the non-working distance travelled by machines operating in a headland field pattern

When treating an area of field using agricultural equipment, the field is usually traversed by a series of parallel tracks using a pattern established by the experience of the operator. At the end of each track the process is constrained by the ability of the operator to distinguish the next track to be followed. The introduction of commercially available auto-steering or navigation-aid systems for agricultural machines has made it possible to upload arbitrary field pattern sequences into programmable navigational computers and for the machines to follow them with precision. This new technology also offers a new perspective for improving machine field efficiency, since not all field traversal sequences are similar in terms of total non-working distance travelled. This paper presents an algorithmic approach towards computing traversal sequences for parallel field tracks, which improve the field efficiency of machines by minimising the total non-working distance travelled. Field coverage is expressed as the traversal of a weighted graph and the problem of finding an optimum traversing sequence is shown to be equivalent to finding the shortest route in the graph. The optimisation is formulated and solved as a binary integer programming problem. Experimental results show that by using optimum sequences, the total non-working distance can, depending on operation, be reduced by up to 50%.