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In cooperative transport systems, multiple robots work together to transport objects that are difficult to transport with a single robot. In recent years, multi-robot systems that cooperate to transport objects have been researched. However, during the transfer of objects, misalignment occurs between the ideal and actual grasp positions. In an automatic transport system, a grasping error can cause an error in the trajectory of the object, significantly reducing the transport efficiency. In this paper, a control system that allows robust cooperative transport control using a model error compensator is proposed for a leader–follower system in which the transported object is the virtual leader and the followers are ideally arranged. This system adds robustness to the operation of a conventional cooperative transport system by using the ideal formation of robots. The effectiveness of the proposed method was evaluated through cooperative transport experiments using two ideal formations for passing through a narrow entrance. The cooperative transport system could not pass through the narrow entrance using the conventional method; however, the system using the compensator passed through the narrow entrance smoothly. [ABSTRACT FROM AUTHOR]

In recent years, personal mobility vehicles have been required to operate autonomously in places with numerous pedestrians. A navigation method using a single human-following scheme is used to avoid collision with pedestrians. However, in many cases, a single human-following method cannot be successfully used for guidance. In crowded places, pedestrians do not always keep walking in the desired direction a user wants to go, and the vehicle must change the target pedestrian frequently. Instead of following a single pedestrian, we propose a method for the vehicle to follow a cluster of pedestrians for stable and robust following. First, the pedestrians around the vehicle are detected by multiple RGB-D cameras, and the pedestrians are tracked using YOLO and Deep Sort. Pedestrians are classified according to their walking direction, and the cluster of pedestrians walking toward the goal is selected and followed. However, the position of pedestrian is sometimes lost in occlusions and the accuracy of the walking direction depends on the distance and pose detected by the sensors. A notable problem is that the cluster of pedestrians is unstable in the cluster following; therefore, a median of candidate vectors (MCV) observer is used to remove outliers caused by observation errors. The proposed method is applied to a scenario involving pedestrians walking toward an elevator hall in a building, and its effectiveness is verified through experiments. [ABSTRACT FROM AUTHOR]

Kumamoto Castle, consisting of towers and stone walls called Ishigaki, was severely damaged by the 2016 Kumamoto earthquake. As this castle has cultural significance in Japan, the damaged stone walls of the castle need to be promptly and accurately restored. In recent years, the utilization of 3D stone data has been studied to determine the position of collapsed stones precisely. The 3D stone data needs to be measured automatically in a short period of time for the efficient restoration of stone walls. To control the attitude of the stone, a small control moment gyro (CMG) device was proposed, and a robust controller with a model error compensator was applied to the CMG device. In this study, the measurement precision of a 3D model of stones using the CMG crane system was analyzed. The automatic measuring system consists of a CMG device and an elevator for a 3D scanner to reduce the load on workers. The scanning motion was planned automatically based on the shape and position of stones using depth sensors. The effectiveness of the proposed measuring system was evaluated using the accuracy and measurement time including the data correction time of the 3D modeling. [ABSTRACT FROM AUTHOR]