Robots in Agriculture: State of Art and Practical Experiences

The presence of robots in agriculture has grown significantly in recent years, overcoming some of the challenges and complications of this field. This chapter aims to collect a complete and recent state of the art about the application of robots in agriculture. The work addresses this topic from two perspectives. On the one hand, it involves the disciplines that lead the automation of agriculture, such as precision agriculture and greenhouse farming, and collects the proposals for automatizing tasks like planting and harvesting, environmental monitoring and crop inspection and treatment. On the other hand, it compiles and analyses the robots that are proposed to accomplish these tasks: e.g. manipulators, ground vehicles and aerial robots. Additionally, the chapter reports with more detail some practical experiences about the application of robot teams to crop inspection and treatment in outdoor agriculture, as well as to environmental monitoring in greenhouse farming

Behavior-Based Control for an Aerial Robotic Swarm in Surveillance Missions

Aerial robotic swarms have shown benefits for performing search and surveillance missions in open spaces in the past. Among other properties, these systems are robust, scalable and adaptable to different scenarios. In this work, we propose a behavior-based algorithm to carry out a surveillance task in a rectangular area with a flexible number of quadcopters, flying at different speeds. Once the efficiency of the algorithm is quantitatively analyzed, the robustness of the system is demonstrated with 3 different tests: loss of broadcast messages, positioning errors, and failure of half of the agents during the mission. Experiments are carried out in an indoor arena with micro quadcopters to support simulation results. Finally, a case study is proposed to show a realistic implementation in the test bed

Comparison of heuristic algorithms in discrete search and surveillance tasks using aerial swarms

The search of a given area is one of the most studied tasks in swarm robotics. Different heuristic methods have been studied in the past taking into account the peculiarities of these systems (number of robots, limited communications and sensing and computational capacities). In this work, we introduce a behavioral network made up of different well-known behaviors that act together to achieve a good performance, while adapting to different scenarios. The algorithm is compared with six strategies based on movement patterns in terms of three performance models. For the comparison, four scenario types are considered: plain, with obstacles, with the target location probability distribution and a combination of obstacles and the target location probability distribution. For each scenario type, different variations are considered, such as the number of agents and area size. Results show that although simplistic solutions may be convenient for the simplest scenario type, for the more complex ones, the proposed algorithm achieves better results.We would like to thank the SAVIER (Situational Awareness Virtual EnviRonment) Project, which is both supported and funded by Airbus Defence & Space. The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and co-funded by Structural Funds of the EU, and from the DPI2014-56985-R project (Protección Robotizada de Infraestructuras Críticas (PRIC)) funded by the Ministerio de Economía y Competitividad of Gobierno de España.Peer Reviewe

Correction to “Bringing Adaptive and Immersive Interfaces to Real-World Multi-Robot Scenarios: Application to Surveillance and Intervention in Infrastructures”

In the above-named article, the affiliation of the authors should have been "Centre for Automation and Robotics (CAR), Universidad Politécnica de Madrid (UPM), 28006 Madrid, Spain.