Dionysis Bochtis

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.

A yield mapping system for hand-harvested fruits based on RFID and GPS location technologies: field testing

It is proposed that radio frequency identification (RFID) technology be used to overcome the limitations of existing yield mapping systems for manual fresh fruit harvesting. Two methods are proposed for matching bins—containing harvested fruits—with corresponding pairs of trees. In the first method, a long-range RFID reader and a DGPS are mounted on an orchard tractor and passive low-cost RFID tags are attached to the bins. In the second method, the DGPS is not used and RFID tags are attached to individual trees as well as bins. An experimental evaluation of the accuracy and reliability of both methods was performed in an orchard. The first method failed in half of the trials because the tree canopies interfered with the GPS signal. The RFID reader miss ratio for the detection of the bins was 0.32% for both methods. However, the attachment of RFID tags on suitable tree branches (to achieve 100% detection), in the second method, is not a well-defined procedure; some trial is demanded to determine the best positions and orientations of the tree tags in order for the RFID reader to successfully detect them. The first method seems more promising if robust tractor location under foliage can be achieved.

A DSS for planning of soil-sensitive field operations

The current increased size of agricultural vehicles aggravates the problem of soil compaction causing increased energy requirements, increased CO2 emissions, and reduced yields. The aim of this paper was to develop a DSS for optimize route planning in terms of minimized risk for soil compaction for agricultural vehicles carrying time-depended loads. The developed system uses as input field and operational characteristics, including a potential risk indicator map based on specific measure of distributed soil physical-chemical properties. It provides the optimal field-work tracks traversal sequence which can be executed using state-of-the-art auto-steering and navigation-aiding systems available on modern agricultural vehicles. The system has been demonstrated and tested for heavy application units used for organic fertilizer. The risk factor was reduced up to 61% by using the corresponding optimal plans instead of the non-optimal conventional ones that an operator would follow. Highlights? Route planning for agricultural vehicles carrying time-depended loads. ? Evaluating potential risk for soil compaction imposed by field traffic. ? Integration of soil-sensitivity indicators and navigation systems.

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.

An optimized field coverage planning approach for navigation of agricultural robots in fields involving obstacle areas

Abstract Technological advances combined with the demand of cost efficiency and environmental considerations has led farmers to review their practices towards the adoption of new managerial approaches, including enhanced automation. The application of field robots is one of the most promising advances among automation technologies. Since the primary goal of an agricultural vehicle is the complete coverage of the cropped area within a field, an essential prerequisite is the capability of the mobile unit to cover the whole field area autonomously. In this paper, the main objective is to develop an approach for coverage planning for agricultural operations involving the presence of obstacle areas within the field area. The developed approach involves a series of stages including the generation of field-work tracks in the field polygon, the clustering of the tracks into blocks taking into account the in-field obstacle areas, the headland paths generation for the field and each obstacle area, the implementation of a genetic algorithm to optimize the sequence that the field robot vehicle will follow to visit the blocks and an algorithmic generation of the task sequences derived from the farmer practices. This approach has proven that it is possible to capture the practices of farmers and embed these practices in an algorithmic description providing a complete field area coverage plan in a form prepared for execution by the navigation system of a field robot.

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%.

Field robotics in sports: automatic generation of guidance lines for automatic grass cutting, striping and pitch marking of football playing fields

Progress is constantly being made and new applications are constantly coming out in the area of field robotics. In this paper, a promising application of field robotics in football playing fields is introduced. An algorithmic approach for generating the way points required for the guidance of a GPS-based field robotic through a football playing field to automatically carry out periodical tasks such as cutting the grass field, pitch and line marking illustrations and lawn striping is represented. The manual operation of these tasks requires very skilful personnel able to work for long hours with very high concentration for the football yard to be compatible with standards of Federation Internationale de Football Association (FIFA). In the other side, a GPS-based guided vehicle or robot with three implements; grass mower, lawn stripping roller and track marking illustrator is capable of working 24 h a day, in most weather and in harsh soil conditions without loss of quality. The proposed approach for the automatic operation of football playing fields requires no or very limited human intervention and therefore it saves numerous working hours and free a worker to focus on other tasks. An economic feasibility study showed that the proposed method is economically superimposing the current manual practices.

Functional modeling for green biomass supply chains

An engineering approach on describing biomass supply chains.Development of models that simulate in detail the operational in-field and transport activities.Identification of bottlenecks in three biomass supply chain systems. The biomass supply chain is a multiple-segment chain characterized by prominent complexity and uncertainty, and as such, it requires increased managerial efforts as compared to the case of a single operation management. This paper deals with the supply chain management of green (e.g. grass) biomass. Specifically, three different supply chain systems, in terms of machinery configurations, were analyzed and evaluated in terms of task times and cost performance. By using a functional modeling methodology, the structural representations of the systems, in terms of activities, actions, processes, and operations, were generated and implemented by the ExtendSim? simulation software. It was shown that the models can identify the bottlenecks of the systems and can be further used as a decision support system by testing various alternatives, in terms of the resources used and their dimensioning. Finally, the models were evaluated against the sensitivity on input parameters which are known with a level of uncertainty, i.e. the expected yield and the expected machinery performance.

In-field logistics processes management based on business activities monitoring systems paradigm

The objective of this paper is to propose a generic architecture framework for an activities monitoring system capable of providing real-time access to critical data and performance indicators for agricultural field operations. It is based on an innovative enhancement of the Business Activity Monitoring (BAM) systems used mainly in the industrial context. The proposed framework architecture has been tested with a specific and real-life harvesting example case. The premise is that agricultural field logistics operations are viewed as a challenging application and development target for emerging and innovative decision-support systems.

Robotic weed monitoring

Abstract In this paper, an integrated management system for the planning and activation of a field monitoring task is presented. The architecture of the system is built around a mobile robotic unit. The internet-based architecture of the system includes a station unit that works as a mobile on-farm operating console, the mobile robotic unit and a field server for generating and storing maps. The hypothesis is that it is possible to automate the planning and execution of the operation of monitoring the in-field weed density and species distribution. The developed planning system includes the automatic field geometrical representation and the route planning for the mobile unit. For the field representation two algorithmic approaches for automated track generation were used. For the route planning, a graph-based field coverage algorithm and a discrete grid-based path planning method were used. The low computational requirements of the implemented algorithms make it feasible to adopt a real-time re-planning strategy in which a set of new planning problems are solved based on the latest information. The central part of such a planning, concerns the dynamic re-evaluation of the initial plan for sampling and routing based on the on-line analysis of the samples. This provides the basis for a fully sequential adaptive adjustment of the sampling procedure after each individual sampling. It is expected that such a dynamic targeted sampling and routing system will reduce the overall cost and time consumption of the weed monitoring operation.