The number of tasks that nowadays are accomplished by using unmanned aerial vehicles is rising across many civil applications, including agriculture. Thus, this work aims at providing the reader with an overview of the agronomical use of unmanned aerial vehicles. The work starts with a historical analysis of the use of aircrafts in agriculture, as pioneers of their use in modern precision agriculture techniques, currently applied by a high number of users. This survey has been carried out by providing a classification of the vehicles according to their typology and main sensorial and performance features. An extensive review of the most common applications and the advantages of using unmanned aerial vehicles is the core of the work. Finally, a brief summary of the key points of the legislation applicable to civil drones that could affect to agricultural applications is analyzed.

The productivity of greenhouses highly depends on the environmental conditions of crops, such as temperature and humidity. The control and monitoring might need large sensor networks, and as a consequence, mobile sensory systems might be a more suitable solution. This paper describes the application of a heterogeneous robot team to monitor environmental variables of greenhouses. The multi-robot system includes both ground and aerial vehicles, looking to provide flexibility and improve performance. The multi-robot sensory system measures the temperature, humidity, luminosity and carbon dioxide concentration in the ground and at different heights. Nevertheless, these measurements can be complemented with other ones (e.g., the concentration of various gases or images of crops) without a considerable effort. Additionally, this work addresses some relevant challenges of multi-robot sensory systems, such as the mission planning and task allocation, the guidance, navigation and control of robots in greenhouses and the coordination among ground and aerial vehicles. This work has an eminently practical approach, and therefore, the system has been extensively tested both in simulations and field experiments.

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.

In this work the authors present a novel algorithm for estimating the odometry of “C” legged robots with compliant legs and an analysis to estimate the pose of the robot. Robots with “C” legs are an alternative to wheeled and tracked robots for overcoming obstacles that can be found in different scenarios like stairs, debris, etc. Therefore, this kind of robot has become very popular for its locomotion capabilities, but at this point these robots do not have developed algorithms to implement autonomous navigation. With that objective in mind, the authors present a novel algorithm using the encoders of the legs to improve the estimation of the robot localization together with other sensors. Odometry is necessary for using some algorithms like the Extended Kalman Filter, which is used for some autonomous navigation algorithms. Due to the flexible properties of the “C” legs and the localization of the rotational axis, obtaining the displacement at every step is not as trivial as in a wheeled robot; to solve those complexities, the algorithm presented in this work makes a linear approximation of the leg compressed instead of calculating in each iteration the mechanics of the leg using finite element analysis, so the calculus level is reduced. Furthermore, the algorithm was tested in simulations and with a real robot. The results obtained in the tests are promising and together with the algorithm and fusion sensor can be used to endow the robots with autonomous navigation.