Against the labor shortage in the restaurant business, automatic dishwashing robots have been being developed. Such the present robots have difficulty of grasping dishes, which causes failure by abnormal grasping. Thus, this paper proposes a method to detect a sign of abnormity from a grasping action through an acceleration sensor attached to the robot.
The proposed method first applies the sliding window for each time point in the sensor timeseries data and obtains plural time-subseries. By SVD of the matrix generated by these subseries, the variation of the sensor value is expressed as a linear subspace. The dissimilarity degree for each time point is calculated from the distance between the corresponding subspaces among two timeseries data. By comparing the average dissimilarity between each normal data and test data with the average dissimilarity degree among normal data, the sigh of abnormity in the test data can be detected.
The experimental result with an actual robot showed that the proposed method enabled detection of the sign of abnormity. It also showed that the cause of abnormity can be distinguished from the term detected as the sign of abnormity during the grasping action.
The purpose of this study was to assess the difference in dynamic stability between young and older adults during normal and fast walking. Position and velocity of the center of mass (COM) were calculated in normal and fast walking in young and older adults. Boundaries of the region of stability were determined based on the COM position and velocity, and the margin to the forward boundary was calculated as the stability margin. Although no significant difference was detected in the stability margin, the COM for the older adults was significantly more anteriorly positioned during fast walking, which seems to result from their significantly shorter step length. These results suggest that older adults are dynamically unstable during fast walking as young are, although they walk with a shorter step.
This paper proposes an autonomous parallel travelling system for an onion harvester and a tractor. Path following control via sliding mode control is applied to each vehicle. The velocity of the tractor is also controlled using the velocity and position of the onion harvester to maintain their relative position. As the result of experiments, the lateral, longitudinal and orientation errors between the tractor and the onion harvester were less than 0.07 m, 0.11 m and 2◦, respectively. It was sufficient for the actual onion harvesting.
Detection of the state changes in the system, which may result in the failure of the system, would be very important in various fields of real-world engineering. We propose a novel unsupervised method for detection of changes in the framework of Energy Based Model (EBM). The proposed method incorporates the Restricted Boltzmann Machine (RBM) and Conditional Restricted Boltzmann Machine (CRBM) as EBM. The state change can be evaluated by observing the free energy in the RBM/CRBM. The proposed method is evaluated by using two sets of time series; one is a sin wave with Gaussian noises and another is the state in a CartPole system. Experimental results show that the free energy is an effective indicator for detecting the state change and that CRBM have better ability for detecting the state change than RBM.
Voronoi diagram is a typical partitioning of plane according to a number of given points on the plane referred to as generators, based on the Euclidean distances from the points. In the current study, a generalization of such voronoi diagram is discussed from the viewpoint of various consideration on distance. On the basis of discrete voronoi decomposition approach, we take into account the various distance metrics other than the conventional Euclidean distance. The existence of a pathway network to shorten the distance gives the space a non-uniformity in distance. We propose an approach for voronoi decomposition under this non-uniformity. Different weights of generators as well as various evaluations of the distance for the voronoi decomposition are also taken into consideration. A number of calculated examples demonstrate the significance of these various conditions on the obtained voronoi diagrams.