Jesús Conesa-Muñoz

Mix-opt

Some of the best known route optimization operators are combined to form a new operator called mix-optMix-opt is intended to hasten route planning for vehicle fleets in agricultural contexts.Mix-opt is configured and tested for both classical CVRP problems and agricultural routing problems.Mix-opt outperforms the operator approaches from which it was developed. To accomplish agriculture tasks, a field is usually divided into tracks based on the implement width. The order in which the crop tracks are covered during this process is critical because it directly affects the distances travelled by the agricultural machines while completing the task and, consequently, soil compaction and inputs. Identifying the best tracks for a set of vehicles to completely cover a field can be formulated as a capacitated vehicle routing problem (CVRP), in which tracks can be viewed as the customers of the CVRP problem. In other words, given a set of n tracks and m vehicles, the objective is to determine a set of routes such that each track is covered exactly once by any of the involved vehicles while minimizing the total cost of covering all the tracks. There are many metaheuristic optimisation methods that address the CVRP problem by using operators to iteratively improve the routes. Most of these operators consist of easy elementary operations such as relocations, swaps or inversions of the order in which customers in the route are visited. In this paper, a new operator, named Mix-opt, is proposed with the aim of accelerating the convergence of metaheuristic optimisation methods and make them less dependent on the operator chosen on routing problems. The proposed operator combines and extends some of the features of the most commonly used route operators by integrating the best-performing elementary operations on which they are based. Further variants of those elementary operations were tested, such as the use of different numbers of elements in the relocations or swaps or reverse orders as well as combining the operations with local searches. The best variants were selected for integration into the proposed operator. Furthermore, Mix-opt was compared against well-established operators by integrating each of them into a Simulated Annealing algorithm and solving well-known CVRP benchmarks and a typical and complex agricultural routing problem. Finally, the proposed operator was applied to be integrated into an agricultural route planner to identify the best routes in some illustrative agricultural problems.All tests demonstrated that Mix-opt, on average, outperforms existing approaches for solving general routing problems as well as a broad spectrum of agricultural routing situations. This helps to better route plan in agricultural contexts, even better than other approaches in a very short time, which is interesting to route plan in real time, for example, because one vehicle may fail during the execution and then it is necessary to route the plan again and very fast to distribute the remaining part of the global task among the rest of the vehicles in the fleet.

Distributed Multi-Level Supervision to Effectively Monitor the Operations of a Fleet of Autonomous Vehicles in Agricultural Tasks

This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system was developed to perform well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weed treatments, by either spraying herbicide or burning pests with a mechanical-thermal actuator. Results are presented for selective weed treatment by the spraying of herbicide. The system successfully supervised the task; it detected failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system was able to prevent collisions between vehicles by taking action to avoid intersecting trajectories. The results show that the proposed system is a highly useful tool for managing fleets of autonomous vehicles. In particular, it can be used to manage agricultural vehicles during treatment operations.

Route planning for agricultural tasks

The proposed route planner addresses a broad range of agricultural problems.The planner considers vehicles with different features and the field variability.The planner optimizes for different criteria, even simultaneously.The planner is validated solving several illustrative problems.The planner outperforms other approaches by up to 17% and 21% in headland distance. Route planning in agricultural fields is a major challenge closely related to the amount of inputs consumed and the associated soil compaction. Current approaches primarily focus on reducing the travelled distances (i.e., the trajectories that vehicles have to cover to carry out the task) and generally do not consider other optimization criteria such as input costs (e.g., fuel, herbicides, labor). Furthermore, although few approaches consider more than one vehicle, none of them takes into consideration vehicles with different characteristics, such as different speeds or different turning radii, and some variabilities of the field such as the weed distribution have not been studied yet. All these factors affect the cost of routes to be followed to accomplish agricultural tasks such as site-specific treatments. In this context, this study proposes a very general approach to optimize the routes that considers: (1) different criteria such as the travelled distance, the time required to perform the task and the input costs, even simultaneously, (2) vehicles with different features (e.g., working speeds, both intra and inter-crop, turning radii, fuel consumptions, tank capacities and spraying costs), (3) the variability of the field and (4) the possibility of tank refilling.The proposed approach has special relevance for route planning in site-specific herbicide applications. This case requires a tank on board the vehicle to store an agrochemical product, and its capacity must be considered because it affects the routes to be followed, specifically in those cases in which the tank capacity may not be sufficient to treat the entire field even when working in cooperation with other vehicles. In such cases, refilling (i.e., a round trip to the refilling depot) may be essential despite the extra cost involved in this operation.The proposed approach was validated by solving several illustrative problems. The results showed that the proposed route planner covers a broad range of agricultural situations and that the optimal routes may vary considerably depending on the features of the fleet vehicles, the variability of the field and the optimization criteria selected. Finally, a comparative study against other well-known agricultural planners was carried out, yielding routes that improved those produced by the reference approaches.

Merge fuzzy visual servoing and GPS-based planning to obtain a proper navigation behavior for a small crop-inspection robot

The concept of precision agriculture, which proposes farming management adapted to crop variability, has emerged in recent years. To effectively implement precision agriculture, data must be gathered from the field in an automated manner at minimal cost. In this study, a small autonomous field inspection vehicle was developed to minimise the impact of the scouting on the crop and soil compaction. The proposed approach integrates a camera with a GPS receiver to obtain a set of basic behaviours required of an autonomous mobile robot to inspect a crop field with full coverage. A path planner considered the field contour and the crop type to determine the best inspection route. An image-processing method capable of extracting the central crop row under uncontrolled lighting conditions in real time from images acquired with a reflex camera positioned on the front of the robot was developed. Two fuzzy controllers were also designed and developed to achieve vision-guided navigation. A method for detecting the end of a crop row using camera-acquired images was developed. In addition, manoeuvres necessary for the robot to change rows were established. These manoeuvres enabled the robot to autonomously cover the entire crop by following a previously established plan and without stepping on the crop row, which is an essential behaviour for covering crops such as maize without damaging them.

Influence of Wind Speed on RGB-D Images in Tree Plantations

Weather conditions can affect sensors’ readings when sampling outdoors. Although sensors are usually set up covering a wide range of conditions, their operational range must be established. In recent years, depth cameras have been shown as a promising tool for plant phenotyping and other related uses. However, the use of these devices is still challenged by prevailing field conditions. Although the influence of lighting conditions on the performance of these cameras has already been established, the effect of wind is still unknown. This study establishes the associated errors when modeling some tree characteristics at different wind speeds. A system using a Kinect v2 sensor and a custom software was tested from null wind speed up to 10 m·s−1. Two tree species with contrasting architecture, poplars and plums, were used as model plants. The results showed different responses depending on tree species and wind speed. Estimations of Leaf Area (LA) and tree volume were generally more consistent at high wind speeds in plum trees. Poplars were particularly affected by wind speeds higher than 5 m·s−1. On the contrary, height measurements were more consistent for poplars than for plum trees. These results show that the use of depth cameras for tree characterization must take into consideration wind conditions in the field. In general, 5 m·s−1 (18 km·h−1) could be established as a conservative limit for good estimations.

A fleet of aerial and ground robots: a scalable approach for autonomous site-specific herbicide application

Trabajo presentado en la 10th European Conference on Precision Agriculture (ECPA 2015), celebrada en Tel-Aviv del 12 al 16 de julio de 2015.

Consolidation of a WSN and Minimax Method to Rapidly Neutralise Intruders in Strategic Installations

Due to the sensitive international situation caused by still-recent terrorist attacks, there is a common need to protect the safety of large spaces such as government buildings, airports and power stations. To address this problem, developments in several research fields, such as video and cognitive audio, decision support systems, human interface, computer architecture, communications networks and communications security, should be integrated with the goal of achieving advanced security systems capable of checking all of the specified requirements and spanning the gap that presently exists in the current market. This paper describes the implementation of a decision system for crisis management in infrastructural building security. Specifically, it describes the implementation of a decision system in the management of building intrusions. The positions of the unidentified persons are reported with the help of a Wireless Sensor Network (WSN). The goal is to achieve an intelligent system capable of making the best decision in real time in order to quickly neutralise one or more intruders who threaten strategic installations. It is assumed that the intruders’ behaviour is inferred through sequences of sensors’ activations and their fusion. This article presents a general approach to selecting the optimum operation from the available neutralisation strategies based on a Minimax algorithm. The distances among different scenario elements will be used to measure the risk of the scene, so a path planning technique will be integrated in order to attain a good performance. Different actions to be executed over the elements of the scene such as moving a guard, blocking a door or turning on an alarm will be used to neutralise the crisis. This set of actions executed to stop the crisis is known as the neutralisation strategy. Finally, the system has been tested in simulations of real situations, and the results have been evaluated according to the final state of the intruders. In 86.5% of the cases, the system achieved the capture of the intruders, and in 59.25% of the cases, they were intercepted before they reached their objective.

Integrating Autonomous Aerial Scouting with Autonomous Ground Actuation to Reduce Chemical Pollution on Crop Soil

Many environmental problems cover large areas, often in rough terrain constrained by natural obstacles, which makes intervention difficult. New technologies, such as unmanned aerial units, may help to address this issue. Due to their suitability to access and easily cover large areas, unmanned aerial units may be used to inspect the terrain and make a first assessment of the affected areas; however, these platforms do not currently have the capability to implement intervention.

Efficient Distribution of a Fleet of Heterogeneous Vehicles in Agriculture: A Practical Approach to Multi-path Planning

Determining the best path planning for an agricultural task is a very important issue in crop management because it directly affects the distances travelled by the agricultural machines and, accordingly, directly affects the soil compaction that occurs and the inputs that are consumed (time and fuel). However, determining the optimal path is a difficult problem because of the large number of variables that must be taken into account: number of vehicles, speeds, turning radii, geometry of the field, tank capacities, fuel consumption, etc. The problem becomes even more difficult when the vehicles are heterogeneous, that is, they perform at different speeds or have different rates of fuel consumption. Furthermore, the optimal multi-path plan may vary depending on the criteria selected, that is, the paths that are optimal for reducing distance may be very inefficient for saving time. In this paper, a practical approach to multi-path planning for fleets of heterogeneous and autonomous vehicles is proposed, considering the optimisation of several criteria such as distance, time, input cost or some combination thereof.

Generating Autonomous Behaviour for a Crop Inspection Robot

This paper presents the main characteristics of a robot whose aim is to perform field inspection using autonomous navigation. The solution developed for crop row tracking is shown, which is a fundamental behaviour for crop inspection. For this purpose, an image processing method is implemented to determine the vehicle’s relative position to the crop row in real time. This position is supplied to two fuzzy controllers, one for angular speed and the other for linear speed. To integrate crop row tracking and other skills that the robot needs, we propose generating the different behaviours of the robot using a network of nodes with different functions: perceptive nodes, cognitive nodes and actuator nodes. The actions of the robot emerge from this set of behaviours, depending on the goals and needs that must be met at each given moment in time.