Recently, education is desired on simultaneous design of control and mechanical systems. However, since the simultaneous design is more difficult than the individual designs, effective supports are necessary. This paper reports some case studies of four years on pursuing appropriate supports for the simultaneous design education.

Currently, instrumental inspections in factories are carried out manually, and it is strongly required to automate the inspections and/or improve the task efficiency. In view of the needs, we consider we address optimal task assignment with a heterogeneous robotic group. First, constraints that have to be met in the robotic inspection task are enumerated and they are represented as logical constraints. Then, the logical constraints are transformed into linear inequalities for appropriately defined binary variables, and the task assignment problem is reduced to an integer programming problem. We further propose a method to reduce the computational burden by dividing the workspace and splitting the optimization problem into an upper layer that allocates regions and a lower layer that allocates targets and tasks. To guarantee the feasibility of the lower layer optimization, new logical constraints are introduced in the upper layer. Finally, a numerical example simulating a real-world working environment confirms the effectiveness of the solution and the reduction in computation time due to the domain partitioning.

We propose smartphone-based operation methods for our conventionally developed “Real World Clicker” (RWC), which is a 3D coordinate measuring device with a TOF-type laser sensor mounted on a pan-tilt actuator. The laser spot coordinate on a target object's surface can be measured with RWC then a support robot can grasp and convey the object to user. Conventionally, RWC has been operated by a PC mouse, but in this paper we attempt to replace this with a smartphone. Specifically, three methods are proposed: (1) a method in which the RWC angle is linked to the posture of the smartphone housing, (2) two-step strategy from coarse to fine tuning that improves on (1), (3) a method in which the sensitivity of the fine tuning operation is optimized based on a robot manipulability. We conduct demonstration experiments to compare the pointing performance of these three methods and conclude that the lastly proposed optimization method based on manipulability has the highest performance.

DOB (Disturbance Observer) and MEC (Model Error Compensator) are known as similar mechanisms to reduce disturbances such as modeling errors and various frictions. This paper discusses the relationship between DOB and MEC for second-order systems without zeros and unstable poles, and the experimental verification of their performance.

We had been under a COVID-19 Pandemic from 2019 to 2022. Our university department gives a course of lectures in one of which first-grade students visit the laboratories in our department. Therefore, we had to plan the laboratory tour by considering the number of students, the preference of students, the temporal restriction, and preventive measures. In this technical note, we formulated this planning as a mixed integer linear programming problem and optimized the laboratory tour by varying several setting parameters. Finally, we conducted the tours based on the optimization result in 2022 and confirmed our approach's usefulness.