Robotic manipulators require contact force sensing capabilities to sense the contact force between the manipulator and an object. Specifically, for humans and robots to coexist in the work environment, the robot must be able to detect an external force applied by a human. This study presents a new intrinsic force sensing method for robot manipulators that can obtain accurate information of the external force applied by a human during human–robot interaction. The method employs a robot cover, which is typically utilized in robot manipulators. Unlike conventional force sensing methods, a six-axis force/torque sensor is placed between the cover and the link of the robot manipulator. As a result, the proposed method provides information of the three-axis contact force applied to the cover surface and its contact location. Therefore, the cover itself becomes a sensorized cover based on the intrinsic force sensing method. To evaluate its sensing performance, the sensorized cover is experimentally validated using reference sensors. Finally, an experiment is performed in which the robot successfully recognizes letters written on the cover, indicating a high level of contact force sensing performance.

In this study, we propose a tomato maturity estimation approach based on a deep neural network. Tomato images were obtained using an RGB camera installed on a monitoring robot and samples were cropped to generate a dataset with which to train the classification model. The classification model is trained using cross-entropy loss and mean–variance loss, which can implicitly provide label distribution knowledge. For continuous maturity estimation in the test stage, the output probability distribution of four maturity classes is calculated as an expected (normalized) value. Our results demonstrate that the F1 score was approximately 0.91 on average, with a range of 0.85–0.97. Furthermore, comparison with the hue value—which is correlated with tomato growth—showed no significant differences between estimated maturity and hue values, except in the pink stage. From the overall results, we found that our approach can not only classify the discrete maturation stages of tomatoes but can also continuously estimate their maturity. Furthermore, it is expected that with higher accuracy data labeling, more precise classification and higher accuracy may be achieved.

In recent years, human-robot cooperation has enhanced productivity and achieved high payload, speed, and accuracy. Integrating typical industrial robots in human-robot cooperation is challenging because their arms may cause serious injuries to humans during a collision due to malfunction or errors due to robot operators. Therefore, counterbalance robot arms that are capable of counterbalancing the gravitational torques due to the robot mass have been developed to decrease the required capacity of the motors and speeds of these robots. In this research, we propose an advanced counterbalance mechanism using gear units and springs to improve the durability and reliability compared to the previously proposed wire-based counterbalance mechanism, which is difficult to apply to a commercialized product because it can easily be broken or stretched when an excessive force is applied for a long period. Moreover, our proposed method was extended to a multi-DOF system using a parallelogram mechanism based on a timing belt and pulleys to achieve multi-DOF robotic arms. A 2-DOF counterbalanced arm was designed to verify the effectiveness of the proposed mechanism. The simulations and experimental results showed that the proposed mechanism effectively reduced the gravitational torques of each joint of the multi-DOF arm.

In this paper, we propose a robust method for estimating the parameters of robot manipulators using the torque separation technique, which was developed previously by the authors, to extract the inertial, gravitational, and frictional components from the input torques of multiple sinusoidal joint motions. The separated components of the input torque produce a set of reduced linear regression equations where the dynamic parameters of the robot manipulators appear decoupled or minimally coupled. A mathematical analysis is presented to show that the set of reduced regression equations tends to yield more robust parameter estimation than the conventional way where a large dimensional full regression equation is solved simultaneously. An iterative scheme to alleviate the possibility of parameter estimation error caused by the deviation from the assumed ideal sinusoidal motions is also proposed. Experimentation with two robot manipulators is used to verify the proposed approach and related claims. The proposed method is simple and pragmatic without requiring any specialized signal filtering and technical know-hows which numerous previous methods often demanded for explicitly or implicitly.

There is an increasing need for manufacturing systems to produce batches in small quantities. Such manufacturing systems are significantly difficult to develop with conventional automation equipment. Recently, several research groups have applied industrial dual‐arm robots to cell production lines. A synchronisation method for robots is necessary for the cell production process when robots work in a shared workspace. Conventional automation factories do not need this method because the main control system operates all machines or robots. However, the intended application for the developed robot is in small manufacturing environments which cannot install an expensive main control system. An inexpensive and high‐performance method with a simple digital in/out channel using EtherCAT is proposed. The developed method was validated for practical use on a cell production line.

Extensive research is being conducted on using robots to automate harvest. However, most of the existing research is focused on the realization of harvesting using a single robot, and there have been very few studies on harvesting and transporting crops from a smart-greenhouse perspective. In this study, we demonstrate that the work efficiency is higher when a plurality of harvesting and transporting robots are used in tandem for harvesting crops in a smart greenhouse, compared to that when a single robot is used. The harvesting and transporting speeds of these robots are modeled in accordance with the facility environment. The operating speed of the robot group comprising only the harvesting robot and the harvesting and transporting robots is derived. In addition, the derived operating speed is analyzed based on the experimental data of the developed harvesting and transporting robots, and it was found that the overall operating speed increased when an appropriate combination of harvesting and transporting robots was used.