Search Results (73)

Agricultural operations such as pepper harvesting, fruit counting, and field phenotyping rely on accurate visual recognition and instance segmentation algorithms. However, pepper fruits in complex field environments often exhibit slender and curved shapes, partial occlusion, ambiguous boundaries, and adhesion between adjacent instances. Existing object detection and instance segmentation methods therefore struggle to obtain complete fruit masks, which adversely affects subsequent fruit counting, contour measurement, and picking-point localization. To improve the instance segmentation accuracy of occluded peppers in complex field scenes, this study proposes OccPepSeg-YOLO, an improved model based on YOLO11n-seg. First, a P2FreqFusion module is introduced to fuse shallow, high-resolution detail features with deep semantic features, thereby enhancing the representation of fruit edges and tip regions. Second, an ASC module is designed to model the directional and scale-related morphological characteristics of pepper fruits, while a BoundaryGate module strengthens responses at occlusion interfaces and boundaries between adjacent instances. Finally, an OccPepSegment multi-scale prototype segmentation head is constructed, and a BDoU loss function is introduced to improve the boundary consistency of instance masks. Experiments on a self-constructed field-pepper instance segmentation dataset showed that OccPepSeg-YOLO achieved M-P, M-R, M-mAP50, and M-mAP50–95 values of 93.87%, 92.09%, 97.17%, and 82.31%, respectively, representing improvements of 5.59, 3.18, 3.83, and 9.52 percentage points over YOLO11n-seg. Further comparisons with representative YOLO-based instance segmentation models, including YOLOv8n-seg, YOLOv9c-seg, YOLO12n-seg, and YOLOv26n-seg, demonstrated that OccPepSeg-YOLO achieved the best overall segmentation performance. In particular, its M-mAP50–95 exceeded the best competing result obtained by YOLOv9c-seg by 8.35 percentage points. Under a unified repeated-inference protocol on an RTX 3090 GPU using FP32 precision, a batch size of 1, and 640 × 640 inputs, OccPepSeg-YOLO achieved a mean inference latency of 15.801 ± 1.238 ms, a P95 latency of 17.323 ms, and a throughput of 63.29 FPS. These results demonstrate that the proposed model can produce more complete pepper instance masks under leaf occlusion, fruit overlap, and complex background conditions, providing technical support for field-pepper recognition, fruit counting, and visual perception by agricultural robots. Full article

Simultaneous localization and mapping (SLAM) is a foundational capability for autonomous navigation in unknown environments. Its performance is strongly coupled to the type, quality, and reliability of available localization and perception sensor data, limiting the portability of navigation systems across heterogeneous mobile robot platforms. This paper presents an adaptive navigation framework designed to support portability across heterogeneous mobile robot platforms by decoupling localization providers from platform-specific localization and perception sensing configurations. A sensor abstraction layer normalizes heterogeneous and low-fidelity sensor localization and perception inputs into a unified representation, enabling structured operational modes constructed according to available sensing modalities, computational constraints, and environmental characteristics. A learning-based performance prediction module is further designed to estimate impending SLAM degradation and support proactive mode switching. Due to middleware constraints within the Pepper NAOqi stack, this predictive component was not deployed during experimental evaluation and remains part of the proposed architecture for future validation. Experimental results on real indoor navigation tasks demonstrate improved robustness and adaptive performance compared with fixed SLAM configurations without manual retuning. Full article

Digital technologies and data-based approaches do not only increase the amount of information available to institutions and the public, they also transform the conditions through which social reality is classified, made visible, ordered, interpreted and governed. This article examines this transformation through AI4Alfieri, a project developed in Italy, at the Luciano Gallino Laboratory of the University of Turin in collaboration with the Fondazione Centro di Studi Alfieriani in Asti, and devoted to generative AI for the mediation of Vittorio Alfieri’s life and work. Specifically, it presents a virtual conversational agent and one embodied in a social robot (Pepper or NAO). The project is analysed as a reflexive case study of a technology in the making, with autoethnographic elements. The empirical corpus comprises a development diary, code and design artefacts, prompts, logs, architectural diagrams and laboratory tests; the study does not evaluate effects on museum visitors. From analysis of the design process, the article advances the interpretive thesis that embodied generative AI makes cultural mediation a problem of distributed accountability: knowledge is governed by the corpus, speech by prompts and retrieval, action by deterministic policy, presence by the body, legitimacy by the institution, and auditability by infrastructures. The paper proposes an exploratory S0–S4 governance stack for embodied generative agents and formulates hypotheses for future research on how data-based cultural mediation may reallocate epistemic authority, institutional legitimacy and design responsibility. Full article

The increasing aging population in Australia necessitates innovative caregiving solutions to address the growing needs of elderly residents. Humanoid robots, with their physical embodiment and human-like attributes, offer a promising technological intervention. This exploratory study investigates the integration of Pepper, a humanoid robot, into aged care facilities, focusing on its potential to meet the needs of older adults and serve as a daily companion, thereby reducing staff workload. The research explores the anthropomorphic features of Pepper, their role in fostering connection and engagement, and the perception and acceptance of the robot as a companion among elderly residents. Findings highlight Pepper’s potential to enhance the quality of care and support in aged care settings while identifying areas for improvement to ensure its successful adoption. Full article

To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. An integrated multi-target tracking and filtering framework is proposed, which fuses multi-feature association, trajectory smoothing and coordinate denoising strategies to suppress depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demonstrate that the proposed system achieves recognition rates of 91.57% and 80.29% and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers. Full article

Accurate classification of fruit maturity is essential for automated grading and robotic manipulation in modern greenhouse cultivation. Most existing methods rely on crop-specific models, severely restricting their scalability in multi-crop scenarios. To overcome this limitation, this study presents SCEA-YOLO, a unified and efficient instance segmentation framework built on YOLOv11s-seg, for simultaneous maturity classification of tomatoes and sweet peppers. To boost feature discrimination, reduce computational redundancy, and alleviate class imbalance, SCEA-YOLO integrates spatial-channel reconstruction convolution and an efficient multi-scale attention mechanism, while replacing the original detection head with the proposed EA-Head. The model is evaluated on a hybrid dataset captured under diverse greenhouse conditions, including varying illumination, fruit occlusion, and overlapping canopies. Its robustness to different viewing angles and camera distances is further validated via deployment on an automated grading robot. Compared with the baseline, SCEA-YOLO enhances classification precision and mAP_50–95 by 5.3% and 2.3% for tomatoes, and 1.2% and 1.4% for sweet peppers, respectively. With only 33.2 GFLOPs, the model satisfies real-time inference demands. Benefiting from its lightweight structure and real-time performance, SCEA-YOLO can be readily deployed on embedded systems and robotic platforms. It offers a practical, unified, and scalable solution for intelligent fruit maturity evaluation in multi-crop greenhouse production. Full article

Localization of a semi-humanoid mobile robot Pepper is proposed based on the particle swarm optimization algorithm (PSO) that is robust to the disturbance perturbations of LIDAR-measured distances from the mobile robot to the walls of the robot real laboratory workspace. The novel PSO, with the avoiding local minima algorithm (PSO-ALM), uses a novel fitness function that can prevent the PSO search from trapping into the local minima and thus prevent the mobile robot from misidentifying the actual location. The fitness function penalizes nonsense solutions by introducing continuous integrity checks of solutions between two different consecutive locations. The proposed methodology enables accurate and real-time global localization of a mobile robot, given the underlying a priori map, with a consistent and predictable time complexity. Numerical simulations and real-world laboratory experiments with different a priori map accuracies have been conducted to prove the proper functioning of the method. The results have been compared with the benchmarks, i.e., the plain vanilla PSO and the built-in robot’s odometrical method, a genetic algorithm with included elitism and adaptive mutation rate (GA), the same GA algorithm with the included ALM algorithm (GA-ALM), the state-of-the-art plain vanilla golden eagle optimization (GEO) algorithm, and the same GEO algorithm with the added ALM algorithm (GEO-ALM). The results showed similar performance with the odometrical method right after recalibration and significantly better performance after some traveled distance. The GA and GEO algorithms with or without the ALM extension gave us similar results according to the accuracy of localization. The optimization algorithms’ performance with added ALM algorithms was much better at not getting caught in the local minimum, while the PSO-ALM algorithm gave us the overall best results. Full article

Agriculture is facing increasing challenges due to labor shortages, rising productivity demands, and the need to operate in unstructured environments. Robotics, particularly soft robotics, offers promising solutions for automating delicate tasks such as fruit harvesting. While numerous soft grippers have been proposed, most focus on grasping and lack the capability to detach fruits with rigid peduncles, which require cutting. This paper presents a novel modular hexagonal soft gripper that integrates soft pneumatic actuators, embedded mechano-optical force sensors for real-time contact monitoring, and a self-centering iris-type cutting mechanism. The entire system is 3D-printed, enabling low-cost fabrication and rapid customization. Experimental validation demonstrates successful harvesting of bell peppers and identifies cutting limitations in tougher crops such as aubergine, primarily due to material constraints in the actuation system. This dual-capability design contributes to the development of multifunctional robotic harvesters capable of adapting to a wide range of fruit types with minimal requirements for perception and mechanical reconfiguration. Full article

This paper presents the design and evaluation of a sound-driven robot quiz system that enhances fairness and engagement in educational human–robot interaction (HRI). The system integrates a real-time sound-based first-responder detection mechanism with gamified multimodal feedback, including verbal cues, music, gestures, points, and badges. Motivational design followed the Octalysis framework, and the system was evaluated using validated scales from the Technology Acceptance Model (TAM), the Intrinsic Motivation Inventory (IMI), and the Godspeed Questionnaire. An experimental study was conducted with 32 university students comparing the proposed multimodal system combined with sound-driven first quiz responder detection to a sequential turn-taking quiz response with a verbal-only feedback system as a baseline. Results revealed significantly higher scores for the experimental group across perceived usefulness (M = 4.32 vs. 3.05, d = 2.14), perceived ease of use (M = 4.03 vs. 3.17, d = 1.43), behavioral intention (M = 4.24 vs. 3.28, d = 1.62), and motivation (M = 4.48 vs. 3.39, d = 3.11). The sound-based first-responder detection system achieved 97.5% accuracy and was perceived as fair and intuitive. These findings highlight the impact of fairness, motivational feedback, and multimodal interaction on learner engagement. The proposed system offers a scalable model for designing inclusive and engaging educational robots that promote active participation through meaningful and enjoyable interactions. Full article

Modern intelligent robotic systems offer farmers a promising solution to labor shortages caused by socio-economic instability and/or pandemics. Efficient harvesting of delicate fruits is one of the main needs in this area. In this context, this work presents a simple and low-cost improvement of the ability of a servo-electric gripper to adjust its force when picking delicate fruits without damaging them. Specifically, this module utilizes a microcontroller that intercepts the current consumed by the servomotor during the gripping action and properly adjusts its aperture, with respect to the force limits suitable for each type of fruit. Experiments were performed on various objects, from elastic balls to oranges, tomatoes and sweet bell peppers. These experiments revealed that the relationship between current consumption and applied force can be accurately approximated by nonlinear expression equations and verified the good performance of the proposed force limitation technique. Consequently, there is scope for adoption by a wide range of agricultural automation systems. Full article

This study aimed to validate the Robot Acceptance Questionnaire (RAQ), a self-report instrument designed to assess user acceptance toward social robots. Originally structured around four theoretical domains—pragmatic, hedonic (identity and feelings), and attractiveness—the RAQ was empirically found to converge into two robust and inversely related dimensions: Positive Attitude (PA) and Negative Attitude (NA). A total of 208 participants (mean = 43.1; S.D. = 21.4) viewed a short video of a humanoid robot (Pepper) and completed the RAQ. Factorial structure (Principal Component Analysis), internal reliability (Cronbach’s alpha), and construct validity were assessed. Results showed excellent internal consistency for both PA and NA (α = 0.93), and intuitive associations with independent measures of ease of use, mastery, and willingness to interact. The RAQ thus offers a concise and reliable tool for assessing general robot acceptance, especially suitable for remote and large-scale studies. Full article

Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences in key quality indicators, and the resulting specific harvesting needs. It then reviews recent progress in intelligent perception, recognition, and automation within the chili pepper industry. For perception and recognition, the review covers the evolution from traditional image processing to deep learning-based methods (e.g., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. The main current research challenges are listed including the environmental adaptability/robustness, efficiency/real-time performance, multi-cultivar adaptability/flexibility, system integration, and cost-effectiveness. Finally, future directions are given (e.g., multimodal sensor fusion, lightweight models, and edge computing applications) in the hope of guiding the intelligent growth of the chili pepper industry. Full article

Large Language Models (LLMs) and Vision-Language Models (VLMs) have the potential to significantly advance the development and application of cognitive architectures for human–robot interaction (HRI) to enable social robots with enhanced cognitive capabilities. An essential cognitive ability of humans is the use of memory. We investigate a way to create a social robot with a human-like memory and recollection based on cognitive processes for a better comprehensible and situational behavior of the robot. Using a combined system consisting of an Adaptive Control of Thought-Rational (ACT-R) model and a humanoid social robot, we show how recollections from the declarative memory of the ACT-R model can be retrieved using data obtained by the robot via an LLM or VLM, processed according to the procedural memory of the cognitive model and returned to the robot as instructions for action. Real-world data captured by the robot can be stored as memory chunks in the cognitive model and recalled, for example by means of associations. This opens up possibilities for using human-like judgment and decision-making capabilities inherent in cognitive architectures with social robots and practically offers opportunities of augmenting the prompt for LLM-driven utterances with content from declarative memory, thus keeping them more contextually relevant. We illustrate the use of such an approach in HRI scenarios with the social robot Pepper. Full article

To address issues such as leaf occlusion, misalignment of the harvesting robotic arm, and limited harvesting range in hillside chili fields, this paper designs an intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling. The system integrates real-time data from a LiDAR and IMU inertial navigation system to reconstruct the chili point cloud occluded by leaves from multiple perspectives. To address issues such as misalignment of the robotic arm caused by terrain undulations, the system integrates an adaptive leveling platform and an H-shaped planar slide, combined with a gyroscope to dynamically adjust the arm’s posture in real time, ensuring arm stability while expanding its workspace. In addition, to ensure harvesting efficiency and pepper integrity, an integrated cutting–gripping flexible end effector is designed to achieve synchronized cutting and collection operations. The experiment shows that the system achieves recognition accuracy of 81.95% for occluded chili peppers and 89.04% for non-occluded chili peppers. The harvesting success rate is 86.33%, with a single harvesting operation taking 13.17 s. During prolonged operation, the harvesting success rate can be maintained at approximately 85.1%. In summary, the intelligent harvesting system based on 3D point cloud reconstruction and multi-mechanism collaborative leveling provides a feasible solution for automated pepper harvesting. Full article

In this paper, a lightweight detection model DSW-YOLO based on improved YOLOv10n is proposed. After comparing mainstream lightweight models (YOLOv5n, YOLOv6n, YOLOv8n, YOLOv9t and YOLOv10n), YOLOv10n with the best performance was selected as the baseline. The DWRR block was then designed and integrated with the C2f module to form C2f-DWRR, replacing the original C2f blocks in the backbone. Consequently, the model’s P, R, mAP50, and mAP50-95 increased by 2.3%, 2.1%, 1.8%, and 3.4%, respectively, while the parameter count dropped by 0.16 M and the model size was reduced by 0.25 MB. A SimAM parameter-free attention mechanism was added to the last layer of the backbone, boosting P, R, mAP50, and mAP50-95 to 90.6%, 84.0%, 91.8%, and 68.5%, and reducing average detection time to 1.1 ms. The CIOU function was replaced with WIOUv3 to accelerate convergence, decrease loss, and significantly enhance detection performance. Experimental results show that on a custom green pepper dataset, DSW-YOLO outperformed the baseline by achieving gains of 2.9%, 2.7%, 2.2%, and 3.4% in P, R, mAP50, and mAP50-95, reducing parameters by 1.6 M, cutting inference time by 0.7 ms, and shrinking the model size to 5.31 MB. DSW-YOLO efficiently and accurately detects green peppers in complex field conditions, significantly improving detection accuracy while remaining lightweight, and provides theoretical and technical support for designing and optimizing pepper-picking robot vision systems. Full article