# Mobile Robotics in Agricultural Operations:
## 1 Introduction  
In the era of Industry 4.0, digitalization of industrial processes and services has been advanced by three key technologies [1]; the Internet of Things (IoT) (e.g., connectivity); additive manufacturing (e.g., 3D printing) and advanced robotics. In particular, the application of robotic systems extend, in principle, from structured industrial environments to semi-structured and even unstructured operational settings [2]. Indicative cases of application include: healthcare operations [3] urban mobility [4]; and precision agriculture [5]. Automating operations in these environments requires technologically advanced robotic systems for performing four main operations [6]: (i) perception; (ii) localization; (iii) planning; and (iv) execution. Similarly, mobile robots, i.e., Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs), are being applied in agriculture for automating a range of labor-intensive, resources-demanding and time-consuming operations. In this regard, planning mobile robots in agriculture is essential within the Operations Research community [7], specifically for resolving constraints of farm input resources, robot maneuverability, agri-field topology, and time windows imposed by the specifications of crops/fresh produce. The extant literature on mobile robots is proliferated by studies which focus on an extensive range of hardware and possible technological options solutions like locomotion systems [8] sensing equipment [9], spatial positioning systems and steering control mechanisms [10]. However, the need for planning mobile robots in agricultural environments, i.e., converting mission specifications as defined by a user into high-level descriptions of how to execute the physical mission subject to defined performance criteria, is also critical and challenging due to:  
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2. the presence of biological entities governed by temporal changes in their properties (the product itself-i.e., the crop-throughout its life continuously changes in terms of physical-e.g., size and shape-and chemical properties-e.g., color and nutrients)
3. the nature of agricultural environments as operational environments are typically non-static, non-certain in terms of robot awareness, and not defined a priori; and
4. the technological and functional constraints of mobile robots. In addition, a major challenge that needs resolution through optimal planning is the limited flight autonomy of UAVs. This hinders the potential for remote sensing and field mapping on a single route [11; 12]. Ensuring safety in remote sensing missions requires careful design of the UAV trajectory in terms of visiting waypoints, flight speed and altitude, flying directions, flexible real-time mission management capacity to minimize the number of changes in trajectory and imagery overlaps, and take-off and landing requirements. Despite the need for planning automated operations in agricultural environments, a generally accepted technical lexicon over mobile robots is lacking as pertinent terms are often used interchangeably. In addition, consistent definition of planning attributes in automated agricultural operations is missing as relevant research is sparse. Therefore, the need to define mobile robots-centric technical aspects and planning should lay the foundation to resolve key research questions (RQs):  
* RQ#1-What is a basic and concise terminology required for planning mobile robotic autonomous operations? * RQ#2-What attributes have to be included to enable a comprehensive locomotion panning in autonomous operations within agri-environments? The objective of this study is to review and address these RQs. Specifically, we adopt a \"narrative\" review approach to provide, as a first step, the basic terminology to describe mobile robotic applications used within field autonomous operations. As a second step, we aim to provide fundamentals planning aspects of mobile robots in agri-field environments. Ultimately these will shape current research, identify knowledge gaps and recommend future directions.