The European Union uses approximately 360 million kg of pesticides per year for agricultural and horticultural tasks (PSS 2015). Assuming that the return in crops saved is approximately four times the investment (Pimentel 2005), these products clearly produce a major economic benefit. However, such assessments do not consider the indirect but important environmental and economic costs associated with pesticide use, which are estimated to total approximately 10 000 million dollars per year in the US (Pimentel 2005). This heavy cost for local and national administrations could be reduced by diminishing pesticide use, which would, in turn, reduce the cost to farmers. The use of precise management and control technologies in agriculture can provide substantial savings of herbicides and pesticides by making use of newly available technologies such as global navigation satellite systems (GNSS), geographic information systems (GIS), automated agricultural machinery, high-resolution image systems, sophisticated sensors, automatic control and robotics. A modern approach is to use existing information and communication technologies (ICT) to design and build improved pest and crop sensors, enhanced actuators and mobile robots to perform proper pest control. Specifically, in recent decades, several attempts to build agricultural autonomous systems for implementing precision agriculture techniques have been carried out and tested under real-world conditions. These efforts have followed different approaches. The first approach has consisted of integrating modified commercial agricultural vehicles and intervention mechanisms (Pilarski et al. 2002; Blackmore et al. 2004; Nørremark et al. 2008; Johnson et al. 2009; Bergerman et al. 2012; Kohanbash et al. 2012; Moorehead et al. 2012). An illustrative example is the work described in Nørremark et al. (2008), which developed an unmanned self-propelled hoeing system for intra-row weed control consisting of an autonomous vehicle based on a commercial agricultural tractor and a cycloid hoe.
A second approach has developed new robot structures (individual steering/traction in the wheels, electrically powered vehicles, adjustable ground clearance and track widths, etc.) and integrated intervention mechanisms for specific agricultural applications. Some of these developments will be operative in the coming years (Bakker et al. 2010; Ruckelshausen et al. 2009).
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One more approach has aimed to build groups of small vehicles under unified control because they can provide clear advantages over existing equipment (Blackmore et al. 2005; Peleg 2005; Cheung et al. 2008; Sørensen and Bochtis 2010). For example, small vehicles ensure higher positioning accuracy during operation and are intrinsically lighter than big machines. This last feature reduces the soil compaction and makes the vehicles safer in terms of safety to others, own safety and crop safety (Blackmore et al. 2001). However, small robots manage smaller payloads, and thus smaller agricultural tools, than do big machines. Therefore, several small robots have to work jointly to accomplish the work of a traditional machine in the same time. This raises the concept of fleets of robots with additional advantages regarding price (it allows farmers to get high-technology equipment in an increasing manner), fault tolerance (failure in a small robot means one less robot at work, while failure in a big vehicle means the whole process on the field is stopped), mission co-ordination and reconfiguration (at any time the fleet behavior can be changed to optimize the mission, taking into account sudden changes in field conditions), etc. (Emmi 2014). The theoretical foundations of fleets of robots have already been investigated (Bautin et al. 2011; Bouraqadi et al. 2012), but the first real tests for applications of precision agriculture techniques have been conducted recently (RHEA 2014). Thus, this article describes one of the works carried out to check fleets of robots.
Within this context, a consortium of 19 experienced multi-disciplinary groups from 15 institutions/companies (3 research centers, 4 universities, 7 small and medium-sized enterprises and one large company) was formed to build, conduct experiments with and evaluate a new generation of automatic systems and fleets of robots for chemical and physical (mechanical and thermal) weed management and tree crop spraying. The project Robot Fleet for Highly Effective Agricultural and Forestry Management (RHEA) was envisaged to change traditional agricultural and forestry production by providing smart, autonomous commercial robots to farms, thus promoting the evolution toward fully automated operations and approaching the concept of the “farm of the future.” The system involves a number of dissimilar ground and aerial robots (Fig. 1) endowed with advanced sensors, innovative end-effectors and improved decision control algorithms that were developed to reduce the use of chemical inputs in agriculture and forestry and to decrease production costs while improving crop quality, health and safety to both humans and animals. Such a large, interdisciplinary project was funded by the European Union under the Seventh Framework Programme.
The project involved the design and development of specific new automatic systems (agricultural tools), mobile robots (aerial and ground) and related equipment (machine vision, communications and location equipment, safety systems, human–machine interfaces, fleet management, etc.), as well as their holistic integration, to accomplish the main tasks of observation, decision making and implementation on croplands to reduce the use of pesticides while improving efficiency.
To achieve the main goal, the activities were organized around a number of specific technical and scientific objectives dedicated to the development of the following:
- (1)Automatic weed mapping systems based on computer vision targeted to detect approximately 90 % of weed patches.The accuracy of vision systems depends on external conditions, which vary considerably in natural environments. Thus detecting 90 % of weeds was considered a reasonable scientific challenge and a profitable figure from the agricultural stand point.
- (2)Decision-making algorithms comprising those specific for co-ordinating the actions of the robots and making them co-operate and collaborate in achieving the tasks. These algorithms include innovative strategies for efficiently scanning terrain with fleets of robots as well as for re-planning the mission after detecting any contingency.The outcomes achieved on these topics are detailed in Conesa-Muñoz et al. (2012) and Emmi et al. (2014).
- (3)Improved agricultural tools for precise real-time treatment to reduce chemical inputs. Three agricultural tools were developed:
- (i)Patch sprayer—The target was to reduce herbicide use by approximately 75 %.Crop fields with weeds covering approximately 25 % of the field area (average-case scenario) need only 25 % of the traditional methods and thus they can save up to 75 % of the herbicide. Nevertheless, this percentage decreases when weed coverage increases.
- (ii)Canopy sprayer—The target was to reduce approximately 50 % in the use of pesticide in canopy spraying.Due to the nature of this sprayer (based on only 4 position-controlled nozzles per side) a reduction of 50 % in the use of pesticide was estimated as an achievable target and economically interesting.
- (iii)Mechanical/thermal tool—the target was to destroy 90 % of the detected weeds.This implement based on liquefied petroleum gas does not leave inputs to the ground and an efficiency for killing 90 % of the detected weeds was selected as an achievable target and attractive enough from the agricultural stand point.
- (i)
- (4)A fleet of aerial robots (unmanned aerial vehicles, UAVs) equipped to acquire images of the task field for post-flight processing.
- (5)A fleet of ground robots (unmanned ground vehicles, UGVs) equipped to apply physical or chemical treatments for pest and weed control (agricultural tools). The robots were equipped with
- (i)
- (ii)Ground perception systems to discriminate weeds from crops in real time, allowing the robots to follow crop rows; the aim was to accurately steer the robots to work on wide-row crops (with 0.75 m-spaced rows).
- (iii)Real-time tree canopy detection systems.
- (6)Graphical user interfaces for monitoring autonomous outdoor vehicles.
- (7)Safety systems for humans and animals near the vehicles and the robots themselves (obstacle detection in the robot’s path).Although the main aim of the RHEA project was to check fleets of robots and all software applications were developed to manage a large number of robots, the limited budget and limited duration of the project forced the consortium to build only 3 UGV and 2 UAV. Thus, in this article, the real RHEA robots will be just referred to as a multi-robot system.
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