Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.

German farmers’ intention to use autonomous field robots: a PLS-analysis

Autonomous field robots are a promising technology for solving several problems in agriculture, as they are electrical driven, can control weeds single-plant based mechanically or with microdoses of pesticides and exert less ground pressure on the field. Whether such robots will be applied on a large scale in German agriculture depends on various parameters. Therefore, the factors influencing the behavioural intention of farmers with respect to their future adoption of autonomous field robots were investigated. The analysis applies a structural equation model based on an extended version of the Unified Theory of Acceptance and Use of Technology. The dataset, collected in 2019, consists of 500 German farmers. The results reveal significantly positive effects of farmers’ expected performance, social influence and trust as well as significantly negative effects of farmers’ effort expectancy and anxiety on the behavioural intention to use autonomous field robots. Additionally, moderating effects of age on the relationship of individual constructs to the behavioural intent to use robots could be confirmed. The results provide important information for various stakeholders. Robot suppliers should better inform farmers about the performance of their products, for instance by involving farmers in the development process of the robots. The ecological benefits attributed to field robots could meet public expectations and should be better communicated to address farmers’ social influence on the behavioural intention to use the robots. Policymakers could try to create better framework conditions, for example by establishing a stable legal situation for autonomous systems or promote its use.

Modular and scalable automation for field robots

This article describes a modular and scalable charging and navigation concept for electrified field robots and other agricultural machines. The concept consists of an underbody charging system on a trailer and a modular navigation box. The underlying conductive charging process is compared to other charging techniques. Charging time in relation to charging current and mean power consumption in field use is displayed. In the navigation box, data of various sensors are combined by means of multi-sensor fusion regarding the precise time of arrival. Time synchronization is achieved by a novel method for compensating the data latency jitter by employing Kalman-based timestamp filtering. Furthermore, navigation functionalities, such as motion planning and mapping, are presented.

German Farmers’ Attitudes on Adopting Autonomous Field Robots: An Empirical Survey

Agricultural production methods in Europe are increasingly subject to public criticism from which many farmers suffer. This applies, among other areas, to the widespread use of pesticides. Autonomous field robots (AFR), as the next stage of agricultural automation, have the potential to farm more intensively and, at the same time, in a more environmentally friendly way. However, a certain skepticism towards autonomous systems is suspected among farmers. Whether farmers adopt a technology depends largely on their uncertainty about the consequences of its use and the resulting attitude on the adoption. In order to quantify the attitude on adopting AFR in Germany and to identify possible group differences within the population, 490 German farmers were surveyed using an online questionnaire, which is based on an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT). In the subsequent cluster analysis, the statements inquiring the intention to use AFR served as cluster-forming variables. As a result, three groups (“open-minded AFR supporters”, “convinced AFR adopters”, “reserved AFR interested”) could be identified according to their response behavior. Despite existing group differences, an overall attitude in favor of autonomous field robots was observed. The results complement the existing research with a further empirical study and provide interesting starting points for further analysis, field robot manufacturers, and political decision makers.

The AgTech Startup Perspective to Farmers Ex Ante Acceptance Process of Autonomous Field Robots

Autonomous vehicles not only provide a new impetus in the development of car models in the automotive industry—even in agriculture there has recently been talk of autonomous field robots (AFR). Great expectations are placed on these digital assistants from a wide variety of perspectives. However, it is still unclear whether they will make the transition from market niches to broad-based distribution. Apart from various factors, this depends on user acceptance of this new technology expected by the innovators, since this is likely to be essential for the further development of AFR. For this purpose, the ex ante user acceptance of farmers from the perspective of various AgTech startups with AFR involvement in Europe was investigated in this exploratory and qualitative study. The Technology Acceptance Model (TAM) served as the basis for the developed interview guideline. In summary, the results confirm that a variety of factors potentially influence farmer acceptance and AFR diffusion from the perspective of AgTech startups, with perceived usefulness being considered the main motivation for using AFR. The interviewed experts believe that AFR will initially be used in crops that have relatively high costs for crop protection treatments before becoming economically attractive for other crops. The basic prerequisite for a successful market launch is an adjustment of the legal framework, which sets standards in relation to AFR and thus, provides security in the production process. The results could support political decision-makers in dealing with this new technology and AFR manufacturers in the promotion of AFR.

Field Robots for Intelligent Farms—Inhering Features from Industry

Estimations of world population growth urgently require improving the efficiency of agricultural processes, as well as improving safety for people and environmental sustainability, which can be opposing characteristics. Industry is pursuing these objectives by developing the concept of the “intelligent factory” (also referred to as the “smart factory”) and, by studying the similarities between industry and agriculture, we can exploit the achievements attained in industry for agriculture. This article focuses on studying those similarities regarding robotics to advance agriculture toward the concept of “intelligent farms” (smart farms). Thus, this article presents some characteristics that agricultural robots should gain from industrial robots to attain the intelligent farm concept regarding robot morphologies and features as well as communication, computing, and data management techniques. The study, restricted to robotics for outdoor farms due to the fact that robotics for greenhouse farms deserves a specific study, reviews different structures for robot manipulators and mobile robots along with the latest techniques used in intelligent factories to advance the characteristics of robotics for future intelligent farms. This article determines similarities, contrasts, and differences between industrial and field robots and identifies some techniques proven in the industry with an extraordinary potential to be used in outdoor farms such as those derived from methods based on artificial intelligence, cyber-physical systems, Internet of Things, Big Data techniques, and cloud computing procedures. Moreover, different types of robots already in use in industry and services are analyzed and their advantages in agriculture reported (parallel, soft, redundant, and dual manipulators) as well as ground and aerial unmanned robots and multi-robot systems.