Precision agriculture requires robotic platforms capable of covering large cultivation areas while providing accurate positioning for localized crop monitoring and management operations. Cable-driven parallel robots (CDPRs) offer large workspaces and lightweight moving structures; however, their usable workspace is constrained by cable tension requirements. This study presents the experimental validation of AgroCableBot, a reconfigurable CDPR for precision agriculture previously proposed by the authors, in which the proximal cable attachment points are vertically reconfigured to increase the wrench-feasible workspace (WFW). Previous simulation results showed that this reconfiguration increases the WFW from approximately 58% to 100% of the geometrically admissible workspace for a minimum cable tension of 5 N. In this work, a physical prototype with adjustable anchor height was built, and a vision-based sensing system was integrated for end-effector localization, automatic calibration, and closed-loop trajectory tracking. Experimental validation included square and circular trajectories and an agricultural inspection trajectory over a seedling tray, executed at three anchor heights (400, 600 and 900 mm). The system achieved a three-dimensional RMSE between 1.9 and 2.6 mm, a 95th-percentile error below 4.5 mm, and a maximum tracking error of 6.0 mm, measured with the vision system. These results demonstrate the potential of AgroCableBot as a reconfigurable robotic platform for accurate crop monitoring and localized agricultural operations in protected cultivation environments.
Automated slide stainers usually protect incubation timing by running one technique at a time or processing slides in fixed batches, which leaves the robotic arm idle while samples incubate. This paper presents the dynamic scheduler of a slide-staining instrument with 42 heated reaction chambers served by a single arm. The scheduler ranks the ready tasks with fixed rules and admits a task only if its predicted execution and the incubations it starts do not collide with committed attendances; durations come from a kinematic model of the arm. In 19 runs on seven instruments, covering immunohistochemistry, immunofluorescence, and in situ hybridization, 4526 of 4528 incubations ended within their 60-s tolerance window, and the model predicted run makespans with a mean error of 1.26%. Simulations in a development environment showed that running heterogeneous protocols together took 28% to 66% less time than processing them in protocol batches, and that the required ranking rules gave a shorter makespan than four classical dispatching rules and a random order in 19 of 20 comparisons. At low load, a CP-SAT lower bound lies about 23% below the makespan, mainly because of deliberate holds that synchronize the preparation of reagent mixtures. Each decision took at most 55 ms.
Quadruped robots are increasingly used for industrial inspection because they can traverse stairs, clutter, and uneven terrain built for people. This paper presents a Systematic Literature Review (SLR), following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines, of autonomous inspection with quadruped robots validated on physical hardware. Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library were searched between 13 and 16 August 2026 for studies published from 2018 to 2026. Of 745 unique records and 1 study found through citation searching, 88 full-text reports were assessed against explicit eligibility criteria and an eight-criterion quality rubric, three criteria of which (mission focus, autonomy integration, and environmental fidelity) acted as eligibility gates; 32 studies were included. They were synthesized narratively by mission control, localization and mapping, navigation, active inspection, and validation approach. Robots reach inspection targets in real facilities, mostly on commercial platforms with vendor or open-source LiDAR localization, but only 4 of the 32 studies let inspection results change the mission, only 3 quantify localization accuracy on hardware, 14 report no statistics over repeated runs, and 27 provide no public code or data. The evidence is limited by single-reviewer selection, short deployments, heterogeneous metrics, and a likely bias toward successful demonstrations.
This paper presents a novel mechanical design framework for robotic manipulators used in precision agriculture operations, specifically vineyard spraying. Objective functions were formulated from the kinematic and dynamic models to assess kinematic dexterity and actuator power requirements, while accounting for the nozzle’s ability to orient at a wide range of angles to target grape clusters and effectively avoid foliage occlusion. A multi-objective optimization framework was formulated to simultaneously optimize kinematic dexterity and the actuators’ dynamic power consumption along operational trajectories. Multi-Objective Evolutionary Algorithms (MOEAs) were used to solve this multi-objective problem, considering eight different 6-DOF manipulator architectures. By applying this framework, the Pareto-optimal fronts for the eight architectures were obtained, enabling a comparative performance analysis of their kinematic and dynamic characteristics. The evaluation reveals that the fully revolute (RRR + wrist) architecture achieves superior balancing, showing a 40% higher Global Posture Index than hybrid joint architectures such as RPR + wrist and RPP + wrist and fully prismatic manipulator PPP + wrist while maintaining a competitive kinematic dexterity and actuator power requirements. Finally, a comprehensive performance analysis of the optimal design was carried out to establish a robust criterion for selecting the most appropriate manipulator architecture for automated agricultural spraying.
With the advancement of exoskeleton technology, the need for accurate adaptation to users’ movements for effective human–exoskeleton interaction is paramount. Therefore, predicting lower-limb joint trajectories, including hip, knee and ankle joints, is a critical aspect of exoskeleton control during locomotion. While joint-angle prediction has traditionally relied on empirical and biomechanical models, machine learning approaches have demonstrated strong capabilities in modeling complex temporal relationships in time-series data. This study presents a comparative evaluation of ARMA (2,2), ARIMA (2,1,2), RNN, LSTM and GRU models for predicting joint angles during human–exoskeleton (HEXO) locomotion. The models were evaluated utilizing R2, MAE, RMSE, training time and inference time. The results showed that the deep learning models consistently outperformed the traditional statistical approaches. Among all of the evaluated models, the RNN achieved the highest predictive accuracy, attaining an R2 ≈ 99.41% for the HEXO hip joint-angle trajectory. Among all of the deep learning architectures, the GRU proved the most favorable balance between prediction accuracy and computational cost. These findings suggest that improvements in predictive accuracy may enhance an exoskeleton’s responsiveness to complex movement patterns and improve biomechanical synchronization. Additionally, the findings provide useful insights for the development of intelligent exoskeleton control systems and identification of opportunities for future research. However, the findings are based on simulated joint-angle data and require validation using experimental exoskeleton datasets before real-world deployment.
