This review reports the recent state of the art in the field of mobile robots applied to precision agriculture. After a brief introduction to precision agriculture, the review focuses on two main topics. First, it provides a broad overview of the most widely used technologies in agriculture related to crop, field, and soil monitoring. Second, the main robotic solutions, with a focus on land-based robots, and their salient features are described. Finally, a short case study about a robot developed by the authors is introduced. This work aims to collect and highlight the most significant trends in research on robotics applied to agriculture. This review shows that the most studied perception solutions are those based on vision and cloud point detection and, following the same trend, most robotic solutions are small robots dedicated exclusively to monitoring tasks. However, the robotisation of other agricultural tasks is growing.

In the last decades, mobile robotics has become a very interesting research topic in the field of robotics, mainly because of population ageing and the recent pandemic emergency caused by Covid-19. Against this context, the paper presents an overview on wheeled mobile robot (WMR), which have a central role in nowadays scenario. In particular, the paper describes the most commonly adopted locomotion strategies, perception systems, control architectures and navigation approaches. After having analyzed the state of the art, this paper focuses on the kinematics of three omnidirectional platforms: a four mecanum wheels robot (4WD), a three omni wheel platform (3WD) and a two swerve-drive system (2SWD). Through a dimensionless approach, these three platforms are compared to understand how their mobility is affected by the wheel speed limitations that are present in every practical application. This original comparison has not been already presented by the literature and it can be used to improve our understanding of the kinematics of these mobile robots and to guide the selection of the most appropriate locomotion system according to the specific application.

In this paper, a novel UGV (unmanned ground vehicle) for precision agriculture, named “Agri.q,” is presented. The Agri.q has a multiple degrees of freedom positioning mechanism and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. In particular, the integration of a 7 DOFs (degrees of freedom) manipulator and a mobile frame results in a reconfigurable workspace, which opens to samples collection and inspection in non-structured environments. Moreover, Agri.q mounts an orientable landing platform for drones which is made of solar panels, enabling multi-robot strategies and solar power storage, with a view to sustainable energy. In fact, the device will assume a central role in a more complex automated system for agriculture, that includes the use of UAV (unmanned aerial vehicle) and UGV for coordinated field monitoring and servicing. The electronics of the device is also discussed, since Agri.q should be ready to send-receive data to move autonomously or to be remotely controlled by means of dedicated processing units and transmitter-receiver modules. This paper collects all these elements and shows the advances of the previous works, describing the design process of the mechatronic system and showing the realization phase, whose outcome is the physical prototype.

Population aging and pandemics have been shown to cause the isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare, and elderly monitoring is one of the services a robot can handle for collective well-being. Despite these emerging needs, in the actual landscape of robotic assistants, there are no platforms that successfully combine reliable mobility in cluttered domestic spaces with lightweight and offline Artificial Intelligence (AI) solutions for perception and interaction. In this work, we present Marvin, a novel assistive robotic platform we developed with a modular layer-based architecture, merging a flexible mechanical design with cutting-edge AI for perception and vocal control. We focus the design of Marvin on three target service functions: monitoring of elderly and reduced-mobility subjects, remote presence and connectivity, and night assistance. Compared to previous works, we propose a tiny omnidirectional platform, which enables agile mobility and effective obstacle avoidance. Moreover, we design a controllable positioning device, which easily allows the user to access the interface for connectivity and extends the visual range of the camera sensor. Nonetheless, we delicately consider the privacy issues arising from private data collection on cloud services, a critical aspect of commercial AI-based assistants. To this end, we demonstrate how lightweight deep learning solutions for visual perception and vocal command can be adopted, completely running offline on the embedded hardware of the robot.

In this paper we propose an innovative solution for a small size hybrid mobile robot called Epi.q-1. Overall dimensions are about 160 mm x 360 mm x 280 mm (height x length x width). The Epi.q-1 robot moves on flat, steep or uneven ground. It can climb over obstacles or steps, even if they are non-uniform in size. Its operating mode adapts to ground conditions and changes accordingly: from rolling (on wheels) to stepping (on legs); thanks to its great mobility it can follow complex routes. It is easy to control with just a few actuators. Robot locomotion drive-generating units employ an original driving mechanism, where degrees of freedom (DOFs) can be limited to some extent according to the operating conditions or thanks to a switching device. Locomotion units consist of a motor linked to a gear (double epicyclical chain) and an axial device (mini-motor and lead screw system) able to lock or unlock some DOFs of the kinematic chain. Moreover, the robot locomotion unit can change its size, from small to large and vice versa, in order to be able to reach restricted spaces and to overcome even quite tall obstacles. It was experimentally tested on flat ground and slopes. It can overcome 90 mm obstacles, that are 72% of the height of the locomotion unit, and climb stairs.

In recent years, the study of robotic systems for agriculture, a modern research field often shortened as “precision agriculture”, has become highly relevant, especially for those repetitive actions that can be automated thanks to innovative robotic solutions. This paper presents the kinematic model and a motion planning pipeline for a mobile manipulator specifically designed for precision agriculture applications, such as crop sampling and monitoring, formed by a novel articulated mobile base and a commercial collaborative manipulator with seven degrees of freedom. Starting from the models of the two subsystems, characterized by an adjustable position and orientation of the manipulator with respect to the mobile base, the linear mapping that describes the differential kinematics of the whole custom system is expressed as a function of the input commands. To perform pick–and–place tasks, a motion planning algorithm, based on the manipulator manipulability index mapping and a closed form inverse kinematics solution is presented. The motion of the system is based on the decoupling of the base and the arm mobility, and the paper discusses how the base can be properly used for manipulator positioning purposes. The closed form inverse kinematics solution is also provided as an open-source Matlab code.