Recorded video data must be clear for accuracy and faster analysis during post-processing, which often requires video stabilization systems to remove undesired motion. In this paper, we proposed a hybrid method to estimate the motion and to stabilize videos by the switching function. This method switched the estimated motion between a Kanade–Lucus–Tomasi (KLT) tracker and an IMU-aided motion estimator. It facilitated the best function to stabilize the video in real-time as those methods had numerous advantages in estimating the motion. To achieve this, we used a KLT tracker to correct the motion for low rotations and an IMU-aided motion estimator for high rotation, owing to the poor performance of the KLT tracker during larger movements. Furthermore, a Kalman filter was used to remove the undesired motion and hence smoothen the trajectory. To increase the frame rate, a multi-threaded approach was applied to execute the algorithm in the array. Irrespective of the situations exposed to the experimental results of the moving camera from five video sequences revealed that the proposed algorithm stabilized the video efficiently.

Soft actuator technology is extensively utilized in robotic manipulation applications. However, several existing designs of soft actuators suffer from drawbacks such as a complex casting process, a multi-air chamber configuration, and insufficient grasping force. In this study, we propose a novel soft bellow design featuring a single air chamber, which simplifies the fabrication process of the actual model. To enhance the performance of the proposed design, we employ the Box–Behnken response surface design to generate a design matrix for implementing different levels of design factors in the finite element model. The FEA response is then subjected to an analysis of variance to identify significant factors and establish a regression model for deformation and stress response prediction. Among the considered responses, the wall thickness emerges as the most influential factor, followed by the divided ratio of radians and the number of bellows. Validation of the optimized soft bellow actuator’s deformation response is performed through comparison with experimental data. Moreover, the soft bellow actuator is capable of exerting a pulling force of 8.16 N when used in conjunction with a simple gripper structure design, enabling effective object manipulation. Additionally, the soft bellow design boasts cost-effectiveness and easy moldability, facilitating seamless integration with different gripper frames for diverse applications. Its simplicity and versatility make it a promising choice for various robotic manipulation tasks.

This paper presents novel 3D synthesis images and a video processing technique for around view vehicle (AVM) monitors. Based on AVM applications used to monitor the situation around a vehicle with limited 2D visual range, we propose increasing the range view by wrapping the 2D AVM image onto the 3D AVM image. The 3D AVM image is a composite of the planar and hemi-spherical models that can be established in a full field of view; this is an integration of the adjustable viewpoint of the 3D AVM image in the perspective view. To increase the efficiency of real-time monitoring, we separate the video into offline and online processes. The offline process involves LUT calculations which are then loaded into the online process. We use a multi-threaded approach to reduce the computation time in the online process, as well. The proposed method is ready to be implemented in a mobile robot prototype before large-scale application. The results presented here indicate that it effectively enhances 3D AVM images in real time.

This research presents a telescopic soft gripper designed to assist farmers in the harvesting of mangosteens. Subsequent to the outbreak of a pandemic, like the novel coronavirus (SARS-CoV-2), producers are encountering a workforce shortage during the harvesting of mangosteen. The labor crisis is currently becoming more acute, but the competition for mangosteen exports is increasing, requiring the expansion of mangosteen agricultural areas, which will ultimately give rise to a labor shortage. The gripper is fabricated from hyperelastic material, a material that offers flexibility and softness, making it ideal for delicate objects. Conventional soft grippers lack the telescopic design, which enables extension and retraction, allowing for the handling of various object sizes. The gripper's design optimization involved the evaluation of nine models with different internal inclinations and material thicknesses. Finite Element Analysis (FEA) was employed to simulate the deformation and stress responses. The optimized model, in comparison to nine other models, possesses a thickness of 2.25 mm and an internal inclination of 10 degrees, facilitating high deformation with acceptable stress. It has been demonstrated that the gripper can deform up to 132 mm, a finding that has been validated through experimentation. The experimental validation was conducted to corroborate these findings, demonstrating the gripper's capacity to securely grasp objects with diameters ranging from 50 mm to 70 mm and weights up to 22 N. Furthermore, the gripper's efficacy was assessed in a mangosteen harvesting scenario, where it demonstrated a capability to successfully harvest the fruit within a span of two seconds. The gripper's design is characterized by its compactness, low production cost, and ease of use, rendering it highly practical for agricultural applications in confined spaces. The telescopic soft gripper under consideration offers a versatile and scalable solution for harvesting a wide range of crops of varying sizes, positioning it as a valuable tool for future agricultural automation.