In recent years, the use of robots has been considered due to the labor shortage. In this situation, the utilization of soft robot grippers that can grasp various objects is expected in the industrial world. The purpose of this study is to develop a soft robotic hand with an adhesion pad attached to a jamming gripper to enable grasping of flat and complex shaped objects. The gripper we have developed can be switched between a jamming gripper and a suction pad by switch control. This makes it possible to perform grasping according to the object. We applied a load to the gripper and conducted a gripping experiment on a complex-shaped object or a flat object. As a result, the gripping success rate of the jamming gripper increased as the applied load increased, and a stable gripping success rate was obtained on the adhesion side. Therefore, it can be seen that the gripper in this study satisfies the sufficient performance.
In this paper, we focus on robotic patrolling systems. Patrolling robots are required to detect changes in environments due to suspicious and missing objects. We have thus far proposed a background subtraction method using two color difference-based edge (CDE) images. However, it is difficult for autonomous mobile robots to acquire the images at the same positions because of the localization error and collision avoidance with obstacles. For the input images in which the backgrounds are different from the standard one, the detection accuracy is decreased. For this problem, we propose to use template matching in the background subtraction method. In the experiments, the patrolling robot successfully detects not only suspicious objects, but also missing objects. Furthermore, the detection accuracy is increased and processing time is reduced compared to a conventional background subtraction method using color edge images. From the results, finally, the effectiveness of the proposed method for the spatial change detection by the patrolling robot is shown.
This study aims to automate the work of picking up objects on the ground for agricultural industries. We design an arm-type mobile robot to effectively pick up objects while considering its compactness, low cost, and usability. Furthermore, we show appropriate force conditions for the end-effector against the ground experimentally. The effectiveness of the proposed system was confirmed by both indoor and outdoor experiment.
For autonomous mobile robot navigation, localization is an essential capability. Given a mobile robot equipped with a 3D LiDAR sensor, an environment map composed of point cloud is built beforehand. The robot is thus allowed to localize the position in the map using the sensor scan data. However, the environment sometimes changes due to obstacles. Under the changing environment, the localization capability of the robot might be decreased. For this challenge, we propose a sensor observation model in a framework of particle filter based localization. In the observation model, we focus on the distance and distribution of point clouds of the map and sensor scan data. In the experiments, a mobile robot is moved by an operator in a virtual environment with obstacles. The robot based on the proposed observation model is able to localize the position in both the original and changing environments with the same accuracy. From the results, we finally show the robustness of the localization capability for changing environments.
Unmanned construction machine has become a promising technology in the civil engineering field. The machine is still less efficient, especially in excavating soil, than the manned construction because the unmanned machine works carefully to avoid potential hazards due to unpredicted soil deformation as well as excavation force. This research aims to predict time-series resistive force of bucket during soil excavation based on the Recurrent Neural Network. First, we experimentally obtain excavating resistive force for various excavation trajectories. In the experiment, a bucket follows pre-determined trajectories with four different elliptical arcs, and the resistive force acting on the bucket is measured. The prediction model of the resistive force is then elaborated by indexing the bucket trajectories. The model validation confirms that the aspect ratio of the ellipse of the trajectories is an effective index for accurately predicting the excavating resistive force.