Search Results (614)

This study investigated the application of Covariant Hamiltonian Optimization for Motion Planning (CHOMP) in the MATLAB programming environment to an industrially relevant Universal Robots UR5e six-DOF manipulator. The parameters were set to be equal to those of the standard MoveIt2 CHOMP plugin, and the obstacle cost was computed directly in MATLAB using the Robotics System Toolbox’s forward kinematics function to obtain the end-effector position at each trajectory waypoint, which was then evaluated against a piecewise potential field defined over three spherical obstacles in the workspace. We executed five distinct picking task examples and one task over thirty trials, together with a sensitivity analysis over the weight parameter defining the optimization smoothness (i.e., weight/gamma). The mixed empirical results exposed major drawbacks of vanilla CHOMP under our parameter configuration. We achieved a collision-free result for only two of the five tasks, T-03 and T-05, with T-03 converging quickly in five iterations (0.16 s) and T-05 requiring 156 iterations and hitting the planning timeout limit of 10 s. Three tasks did not yield any collision-free result under the 10 s time limit. One of those three tasks, T-01, when running 30 random trial simulations after adding a tiny amount of noise to the start/end poses, yielded 0%, so all trials timed out on its planning 200-iteration limit with an invalid collision result. We analyzed the movement profile (position, velocity, and acceleration over time) of the trajectories generated during the experiments. Several examples exceed the UR5e velocity limit (180 deg/s) and the UR5e acceleration limit (400 deg/s²) by an order of magnitude, and peak values on T-02 reached up to 4731.92 deg/s². With these chosen parameters, basic CHOMP is not industrially suitable for the UR5e robot or for the implementation of the empirical evaluation of CHOMP discussed in this paper. We also identified the modes of failure of basic CHOMP under these parameters and discuss relevant changes. Full article

To address the visual perception challenges in automated harvesting of the ‘Sucui No.1 Pear’, this study proposes a fast detection and 3D localization method based on an improved YOLOv11 architecture and an RGB-D camera. First, a multi-scene ‘Sucui No.1 Pear’ dataset containing 5842 RGB-D image pairs and 31,559 labeled instances was constructed. Second, a lightweight YOLOv11-pear detection model optimized for pear fruit was developed; by reconstructing the feature pyramid network and introducing a global attention mechanism, using K-means++ clustering to optimize prior anchor boxes, and designing a composite loss function (Varifocal Loss + CIoU Loss + DFL), the detection accuracy was improved while maintaining lightweight design. The model adopts a “detect first, then fuse” strategy, achieving robust 3D coordinate calculation based on the median depth of the bottom region of the detection box. Experimental results show that YOLOv11-pear achieves 93.8% mAP@0.5 and 65.5% mAP@0.5:0.95 on the independent test set, with a precision of 94.2% and a recall of 91.5%. The model has only 5.8 M parameters and achieves real-time inference at 38.7 FPS on the Jetson Orin NX edge platform, with a mean absolute error of 9.9 mm for 3D localization. In severely occluded and complex lighting scenarios, the mAP@0.5 reaches 80.9% and 87.5%, respectively. After integrating the vision system into the harvesting robot platform, end-to-end closed-loop testing in a real orchard achieved an 88% harvesting success rate and 5% fruit damage rate. The average time for the visual perception stage was 1.3 s, accounting for 9.4% of the harvesting cycle. This research provides a high-precision, lightweight, and deployable vision solution for automated harvesting of the ‘Sucui No.1 Pear’, and has important reference value for promoting the development of intelligent fruit harvesting technology. Full article

Apple harvesting is transitioning from conventional manual picking to harvest-assist platforms, vibration-based mechanical systems, and selective robotic harvesting. For fresh-market apples, harvesting efficiency cannot be evaluated independently of mechanical damage, postharvest quality deterioration, and food-safety risks. Compression bruising, impact bruising, abrasion, cuts, punctures, and stem-end tearing can disrupt the peel, cuticle, and cellular structure to different degrees, triggering reactive oxygen species accumulation, membrane lipid peroxidation, cell-wall degradation, enzymatic browning, and increased respiration and ethylene metabolism. These responses may subsequently be amplified during storage, transportation, and processing, leading to softening, decay, shortened shelf life, and potential patulin contamination. Centered on the damage–safety trade-off, this review compares the technical characteristics and typical damage profiles of manual harvesting, vibration-based mechanical harvesting, harvest-assist platforms, and selective harvesting robots; explains how mechanical damage is transmitted from cellular physiological responses to food-quality and safety outcomes; and proposes conceptual frameworks for a Damage Risk Index and a Harvest Suitability Score. The central objective of intelligent apple harvesting should therefore be to improve efficiency while preserving appearance, texture, nutritional quality, storage stability, and food safety. Full article

To address the limitations of current Agaricus bisporus harvesting robots, including low picking efficiency, susceptibility to mechanical damage, poor operational stability, and discontinuous harvesting processes, an integrated robotic system capable of mushroom detection and localization, picking sequence planning, stipe-cutting, and grading was developed. The robot adopts a left-right symmetrical configuration and operates along the side of the mushroom cultivation racks. It mainly consists of a mobile platform, a lifting mechanism, dual picking units, a receiving unit, a stipe-cutting device, a collection system, and an electronic control system. A picking sequence planning method based on YOLOv8n-USD and KD-tree nearest neighbor search was employed to determine the optimal harvesting order for densely clustered and adhered mushrooms. The dual robotic arms executed the picking operations, after which the mushrooms were transferred to the receiving unit for stipe-cutting and subsequently classified according to the detection results, thereby completing the integrated process of detection, picking, stipe-cutting, and grading. Experimental results show that the average detection accuracy reaches 96.64%, the picking success rate is 95.39%, and the harvesting damage rate is 1.63%, while the average interaction failure rate and grading error rate are 0.35% and 1.28%, respectively. These results indicate that the proposed robot can achieve accurate detection and efficient integrated operation for densely clustered Agaricus bisporus. The findings provide a reference for flexible and efficient harvesting of Agaricus bisporus with synchronized stipe-cutting and grading operations. Full article

The terrain of tea gardens in Yunnan is complex, and the branches and leaves of tea trees are densely interlaced. Traditional manual picking methods are labor-intensive and inefficient, and can no longer meet the needs of modern tea industry development. To achieve automatic recognition of tea buds and leaves and accurate two-dimensional localization of picking points in complex natural environments, this study constructs a lightweight and high-precision tea bud and leaf detection and keypoint localization model, YOLOv11-pose-BEH, based on the YOLOv11-pose model. Based on the original network, this model introduces the BiFPN feature fusion module to achieve efficient bidirectional transmission of multi-scale information. The EMA (Efficient Multi-scale Attention) mechanism is integrated to form the C2PSA_EMA module, improving the effect of multi-scale feature extraction. HetConv is introduced to form the C3K2_HetConv module, enhancing the extraction of local texture and edge features. Compared with the baseline network, the improved YOLOv11-pose-BEH achieved significant improvements in both tea bud object detection and picking point localization. For the tea bud object detection task, the precision, recall, mAP0.5, and F1-score increased by 7.41, 6.39, 5.28, and 6.88 percentage points, respectively. For the picking point localization task, the precision, recall, mAP0.5, and F1-score increased by 5.86, 5.83, 4.83, and 5.85 percentage points, respectively. These results indicate that the proposed model can achieve more accurate and stable tea bud and leaf detection and two-dimensional picking point localization under complex backgrounds, providing efficient and reliable technical support for the visual perception of intelligent tea-picking robots in tea gardens. Full article

The objective of this feasibility study was to demonstrate and evaluate a remotely tongue-controlled wheelchair mounted assistive robotic manipulator (ARM), for the first time with end users: three individuals with cervical spinal cord injury. For three days, they remotely tongue-controlled the wheelchair and the ARM (WMARM) to complete two activities of daily living (ADL): Driving the wheelchair and ARM to a remote setting to (1) pick up a bottle and (2) pick up a ball. The participants controlled the system using full manual control by tongue and through semi-automation. Finally, the participants answered a NASA Task Load Index (TLX) questionnaire and a semi-structured interview. Results: All participants were able to remotely control the WMARM by tongue. Semi-automation resulted in shorter task completion time, gripping time and fewer commands as compared with manual control. Semi-automation decreased the measured mental load in the NASA TLX questionnaire by an average of 57%. The participants rated high satisfaction with the system. Conclusion: It was possible for the users with tetraplegia to control the wheelchair with the ARM using their tongue to perform ADL in Wi-Fi-based remote setting. This proposed system has the potential to increase independence and social interaction of individuals with tetraplegia. Full article

Motion optimization has a significant influence on performance and robustness of modern robotic handling systems. In this study, a pick-and-place operation, as can be found in many processing machines, serves as a representative use case to develop a novel method for motion optimization. Based on optimal control theory, the introduced method uses bilevel optimization simultaneously addressing process stability, favorable dynamic behavior and practical applicability. A process model for gripper load estimation established in the literature serves both as a basis for optimization and for evaluating the solution found. To establish a benchmark, a spline-based trajectory is generated. As this work proposes a theoretical approach, simulations based on the retrieved model serve as an evaluation basis of the resulting trajectories. The results show that a significant reduction in gripper load by approximately 60% was achieved compared to the reference motions. Eventually, requirements and limitations for application of the new method are discussed. Full article

Reliable selective picking of long-staple cotton remains challenging because dense dwarf canopies restrict robot operating space and increase boll occlusion, resulting in reduced target visibility and potential fiber damage during picking. To address these challenges, a mobile dual-arm robotic picking system integrating hierarchical depth perception, cotton-boll recognition, optimized motion planning, and three-finger flexible end-effectors was developed for autonomous picking in Xinjiang long-staple cotton fields. The proposed YOLOv7-DCN-SENet model reached 95.75% precision, 92.65% recall, and 97.19% mAP@0.5 on the test set, while the onboard computing platform operated at 101 FPS under the experimental configuration. Indoor and field experiments were conducted on directly visible upper-canopy open cotton bolls. The dual-arm robot achieved parallel picking success rates of 74.6% and 57.6%, with average picking cycles of 28.2 s and 34.9 s, respectively. Field performance was mainly limited by strong-light overexposure, depth-information loss, occlusion-induced localization errors, arm interference within narrow canopy spaces, and incomplete fiber separation during boll detachment. These results demonstrate the feasibility of autonomous dual-arm selective picking for long-staple cotton under dense planting conditions and provide a basis for further improvements in robotic cotton-picking systems. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Recent advancements in Vision-Language Models (VLMs) have enabled robotic systems to leverage model-based understanding and reasoning over visual and linguistic inputs, offering a promising approach for interpreting user intent in human–robot interaction (HRI). In particular, deictic expressions commonly used in object handovers, such as “take this” and “give me that”, cannot be fully interpreted through language alone and require a comprehensive understanding of the speaker’s perspective and the environment. This study proposes a prompt-driven vision-language framework for deictic interpretation in human–robot handover. The system integrates a pre-trained VLM with a hierarchical prompt that decomposes reasoning into intent classification, spatio-temporal grounding, and output self-validation, enabling accurate identification of target objects and goal locations without model fine-tuning. Experimental results demonstrate 100% command interpretation accuracy across multiple interaction scenarios, including pick-and-place tasks, robot-to-human and human-to-robot handovers, and temporal deictic commands. Notably, the system operates under a prompt–command language mismatch, accurately interpreting Korean commands while being guided by English-based prompts. Analysis across progressive system configurations further demonstrates that structured prompting plays a critical role in reasoning performance. These results highlight the effectiveness of a prompt-driven approach for deictic interpretation and spatio-temporal grounding, providing a practical training-free framework for HRI. Full article

Long-horizon mobile manipulation requires a robot to execute a sequence of heterogeneous subtasks such as navigation, picking, and articulated-object manipulation in indoor environments. Standard reinforcement learning suffers from reward sparsity and inefficient exploration in this setting, and hierarchical methods often fail at the hand-off between consecutive subtasks when the terminal state of one subtask is kinematically infeasible for the next. We propose a motion planning-augmented hierarchical reinforcement learning architecture to resolve the fundamental trade-offs between sample efficiency and hand-off reliability in long-horizon mobile manipulation. The mission is decomposed into subtasks via a Semi-Markov Decision Process; within each subtask, a collision-free reference trajectory generated by RRT* in the full joint configuration space is embedded into the reward as a per-step shaping signal; and a region-goal mechanism, defined analytically from inverse kinematics feasibility, replaces rigid coordinate hand-offs with a continuous feasible region. The architecture is evaluated in the ManiSkill-HAB simulation under teleport-free sequential execution and challenging initialization. The proposed method improves subtask success rate and sample efficiency over the baseline across all six evaluated subtasks, and the advantage compounds along the long-horizon task chain. Full article

Robust manipulation of small, densely stacked objects remains a challenging problem due to severe occlusions and geometric ambiguities, particularly under single-view sensing conditions. When observed using a single RGB-D sensor, adjacent surfaces of featureless cuboid objects, such as Jenga blocks, often merge in depth measurements, making reliable instance separation and pose estimation difficult. This paper presents a strategy-driven perception and manipulation framework for the robotic rearrangement of randomly stacked Jenga blocks under single RGB-D sensor constraints. The proposed approach employs a heightmap-based perception pipeline that integrates color filtering with geometric reasoning to segment individual blocks and estimate manipulation-compatible poses. Beyond perception, the proposed system determines robot actions through a structured manipulation policy consisting of region-wise search for directly executable grasps, grasp candidate evaluation based on accessibility and collision risk, selective local regrasping for workspace reconfiguration, and placement mode selection between direct insertion and sliding-assisted placement. In this framework, controlled grasp-and-release actions are applied only when no directly executable candidate is found within the currently scanned region and a suitable recovery target can be identified, thereby transforming cluttered local arrangements into more executable states without requiring additional sensing modalities. Experimental results, conducted under competition-equivalent conditions, demonstrate a high task success rate of 99.02%, confirming the robustness and reliability of the proposed framework. The results show that strategy-driven manipulation can effectively compensate for perception limitations in single RGB-D sensor environments, enabling stable and efficient pick-and-place operations in dense clutter. Full article

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

Agriculture is currently transitioning toward higher intelligence and facility-based production, where harvesting robots play a crucial role in enhancing efficiency and ensuring standardized output. Addressing the challenges of inaccurate picking pose estimation and limited reachability in greenhouse environments, this paper proposes a reachable grasping pose estimation method based on Particle Swarm Optimization (PSO). First, initial poses are calculated via instance segmentation and keypoint extraction. Subsequently, a fitness function is constructed based on inverse kinematics, and the PSO algorithm is employed to iteratively search for optimal reachable poses. To further tackle planning difficulties in confined spaces, a two-stage path planning method based on cost maps is introduced. A series of performance metrics were designed to validate the proposed pose estimation and path planning methods through simulation experiments. In real-world field tests, the system achieved a harvesting success rate of 85%, significantly outperforming existing methods. The results demonstrate that the proposed approach substantially enhances the operational feasibility and success rate of tomato harvesting robots. Full article