This paper introduces the R‐ECPS (Robotics‐focused Eco‐Cyber‐Physical System) framework for the development of robotics‐enabled digital twins, integrating human knowledge into the decision‐making process. Our framework adapts ECPS and Digital Twin (DT) definitions to agricultural field robotics. We illustrate its application by envisioning an autonomous digital twin for smart livestock management. The presented preliminary implementation includes a digital testbed, an autonomous environmental parameter mapping algorithm, and a foraging model. Development choices are the result of interdisciplinary collaboration across field robotics, biology, and nutrition. Initial results from a proof‐of‐concept trial reveal both challenges and opportunities for improvement. Despite limitations in the use case early implementation, our framework provides a roadmap for future steps and aims at laying the foundation for advancing robotics‐focused agricultural digital twins.

In agriculture, understanding the distribution and relationship between different aspects of the environment is important for minimizing chemical use and reducing environmental impact. Traditionally, it is done by manually collecting samples on the field and then sending them to a laboratory for analysis. This is not only labor-intensive and costly, but the results will still be outdated. There is thus a growing interest in developing robotic systems to map these variables and uncover their correlations in real time. However, existing learning and sampling methods only focus on one quantity of interest (QoI) or make assumptions that might lead to sub-optimal results when there are multiple QoIs. In this work, we propose a multivariate active transfer learning and intelligent adaptive sampling system that can simultaneously learn the most accurate models for multiple QoIs as well as the relationship between them, and leverage that knowledge to select the next best locations to sample. Performance benchmarking against existing methods shows that QoIs are mapped more accurately, complex correlations between QoIs are identified more precisely, and travel routes are planned more efficiently.

In open-pit mining, holes are drilled into the surface of the excavation site and are then detonated with explosives to facilitate digging. These blast holes need to be inspected internally for quality assurance, as well as for operational and geological reasons. Manual hole inspection is slow and expensive, limited in its ability to capture the geometric and geological characteristics of holes. This is the motivation behind the development of our autonomous mine site inspection robot—“DIPPeR”. In this paper, the automation aspect of the project is explained. We present a robust navigation and perception framework that provides streamlined blasthole detection, tracking, and precise down-hole sensor insertion during repetitive inspection tasks. To mitigate the effects of noisy GPS and odometry data typical of surface mining environments, we employ a proximity-based adaptive navigation system that enables the vehicle to dynamically adjust its operations according to target detectability and localisation accuracy. For perception, we process LiDAR data to extract the cone-shaped volume of drill waste above ground, and then project the 3D cone points into a virtual depth image to form accurate 2D segmentation of hole regions. To ensure continuous target tracking as the robot approaches the goal, our system automatically adjusts the projection parameters to ensure consistent appearance of the hole in the image. In the vicinity of the hole, we apply least squares circle fitting combined with non-maximum candidate suppression to achieve accurate hole localisation and collision-free down-hole sensor insertion. We demonstrate the effectiveness and robustness of our framework through dedicated perception and navigation feature tests, as well as streamlined mission trials conducted in high-fidelity simulations and real mine-site field experiments.